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Lithographic Stochastic Limits on Resolution

Monday, April 3rd, 2017

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By Ed Korczynski, Sr. Technical Editor

The physical and economic limits of Moore’s Law are being approached as the commercial IC fab industry continues reducing device features to the atomic-scale. Early signs of such limits are seen when attempting to pattern the smallest possible features using lithography. Stochastic variation in the composition of the photoresist as well as in the number of incident photons combine to destroy determinism for the smallest devices in R&D. The most advanced Extreme Ultra-Violet (EUV) exposure tools from ASML cannot avoid this problem without reducing throughputs, and thereby increasing the cost of manufacturing.

Since the beginning of IC manufacturing over 50 years ago, chip production has been based on deterministic control of fabrication (fab) processes. Variations within process parameters could be controlled with statistics to ensure that all transistors on a chip performed nearly identically. Design rules could be set based on assumed in-fab distributions of CD and misalignment between layers to determine the final performance of transistors.

As the IC fab industry has evolved from micron-scale to nanometer-scale device production, the control of lithographic patterning has evolved to be able to bend-light at 193nm wavelength using Off-Axis Illumination (OAI) of Optical-Proximity Correction (OPC) mask features as part of Reticle Enhancement Technology (RET) to be able to print <40nm half-pitch (HP) line arrays with good definition. The most advanced masks and 193nm-immersion (193i) steppers today are able to focus more photons into each cubic-nanometer of photoresist to improve the contrast between exposed and non-exposed regions in the areal image. To avoid escalating cost and complexity of multi-patterning with 193i, the industry needs Extreme Ultra-Violet Lithography (EUVL) technology.

Figure 1 shows Dr. Britt Turkot, who has been leading Intel’s integration of EUVL since 1996, reassuring a standing-room-only crowd during a 2017 SPIE Advanced Lithography (http://spie.org/conferences-and-exhibitions/advanced-lithography) keynote address that the availability for manufacturing of EUVL steppers has been steadily improving. The new tools are close to 80% available for manufacturing, but they may need to process fewer wafers per hour to ensure high yielding final chips.

Figure 1. Britt Turkot (Intel Corp.) gave a keynote presentation on "EUVL Readiness for High-Volume Manufacturing” during the 2017 SPIE Advanced Lithography conference. (Source: SPIE)

The KLA-Tencor Lithography Users Forum was held in San Jose on February 26 before the start of SPIE-AL; there, Turcot also provided a keynote address that mentioned the inherent stochastic issues associated with patterning 7nm-node device features. We must ensure zero defects within the 10 billion contacts needed in the most advanced ICs. Given 10 billion contacts it is statistically certain that some will be subject to 7-sigma fluctuations, and this leads to problems in controlling the limited number of EUV photons reaching the target area of a resist feature. The volume of resist material available to absorb EUV in a given area is reduced by the need to avoid pattern-collapse when aspect-ratios increase over 2:1; so 15nm half-pitch lines will generally be limited to just 30nm thick resist. “The current state of materials will not gate EUV,” said Turkot, “but we need better stochastics and control of shot-noise so that photoresist will not be a long-term limiter.”

TABLE:  EUVL stochastics due to scaled contact hole size. (Source: Intel Corp.)

CONTACT HOLE DIAMETER 24nm 16nm
INCIDENT EUV PHOTONS 4610 2050
# ABSORBED IN AREAL IMAGE 700 215

From the LithoGuru blog of gentleman scientist Chris Mack (http://www.lithoguru.com/scientist/essays/Tennants_Law.html):

One reason why smaller pixels are harder to control is the stochastic effects of exposure:  as you decrease the number of electrons (or photons) per pixel, the statistical uncertainty in the number of electrons or photons actually used goes up. The uncertainty produces line-width errors, most readily observed as line-width roughness (LWR). To combat the growing uncertainty in smaller pixels, a higher dose is required.

We define a “stochastic” or random process as a collection of random variables (https://en.wikipedia.org/wiki/Stochastic_process), and a Wiener process (https://en.wikipedia.org/wiki/Wiener_process) as a continuous-time stochastic process in honor of Norbert Wiener. Brownian motion and the thermally-driven diffusion of molecules exhibit such “random-walk” behavior. Stochastic phenomena in lithography include the following:

  • Photon count,
  • Photo-acid generator positions,
  • Photon absorption,
  • Photo-acid generation,
  • Polymer position and chain length,
  • Diffusion during post-exposure bake,
  • Dissolution/neutralization, and
  • Etching hard-mask.

Figure 2 shows the stochastics within EUVL start with direct photolysis and include ionization and scattering within a given discrete photoresist volume, as reported by Solid State Technology in 2010.

Figure 2. Discrete acid generation in an EUV resist is based on photolysis as well as ionization and electron scattering; stochastic variations of each must be considered in minimally scaled areal images. (Source: Solid State Technology)

Resist R&D

During SPIE-AL this year, ASML provided an overview of the state of the craft in EUV resist R&D. There has been steady resolution improvement over 10 years with Photo-sensitive Chemically-Amplified Resists (PCAR) from 45nm to 13nm HP; however, 13nm HP needed 58 mJ/cm2, and provided DoF of 99nm with 4.4nm LWR. The recent non-PCAR Metal-Oxide Resist (MOR) from Inpria has been shown to resolve 12nm HP with  4.7 LWR using 38 mJ/cm2, and increasing exposure to 70 mJ/cm2 has produced 10nm HP L/S patterns.

In the EUVL tool with variable pupil control, reducing the pupil fill increases the contrast such that 20nm diameter contact holes with 3nm Local Critical-Dimension Uniformity (LCDU) can be done. The challenge is to get LCDU to <2nm to meet the specification for future chips. ASML’s announced next-generation N.A. >0.5 EUVL stepper will use anamorphic mirrors and masks which will double the illumination intensity per cm2 compared to today’s 0.33 N.A. tools. This will inherently improve the stochastics, when eventually ready after 2020.

The newest generation EUVL steppers use a membrane between the wafer and the optics so that any resist out-gassing cannot contaminate the mirrors, and this allow a much wider range of materials to be used as resists. Regarding MOR, there are 3.5 times more absorbed photons and 8 times more electrons generated per photon compared to PCAR. Metal hard-masks (HM) and other under-layers create reflections that have a significant effect on the LWR, requiring tuning of the materials in resist stacks.

Default R&D hub of the world imec has been testing EUV resists from five different suppliers, targeting 20 mJ/cm2 sensitivity with 30nm thickness for PCAR and 18nm thickness for MOR. All suppliers were able to deliver the requested resolution of 16nm HP line/space (L/S) patterns, yet all resists showed LWR >5nm. In another experiment, the dose to size for imec’s “7nm-node” metal-2 (M2) vias with nominal pitch of 53nm was ~60mJ/cm2. All else equal, three times slower lithography costs three times as much per wafer pass.

—E.K.

Deep Learning Could Boost Yields, Increase Revenues

Thursday, March 23rd, 2017

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By Dave Lammers, Contributing Editor

While it is still early days for deep-learning techniques, the semiconductor industry may benefit from the advances in neural networks, according to analysts and industry executives.

First, the design and manufacturing of advanced ICs can become more efficient by deploying neural networks trained to analyze data, though labelling and classifying that data remains a major challenge. Also, demand will be spurred by the inference engines used in smartphones, autos, drones, robots and other systems, while the processors needed to train neural networks will re-energize demand for high-performance systems.

Abel Brown, senior systems architect at Nvidia, said until the 2010-2012 time frame, neural networks “didn’t have enough data.” Then, a “big bang” occurred when computing power multiplied and very large labelled data sets grew at Amazon, Google, and elsewhere. The trifecta was complete with advances in neural network techniques for image, video, and real-time voice recognition, among others.

During the training process, Brown noted, neural networks “figure out the important parts of the data” and then “converge to a set of significant features and parameters.”

Chris Rowen, who recently started Cognite Ventures to advise deep-learning startups, said he is “becoming aware of a lot more interest from the EDA industry” in deep learning techniques, adding that “problems in manufacturing also are very suitable” to the approach.

Chris Rowen, Cognite Ventures

For the semiconductor industry, Rowen said, deep-learning techniques are akin to “a shiny new hammer” that companies are still trying to figure out how to put to good use. But since yield questions are so important, and the causes of defects are often so hard to pinpoint, deep learning is an attractive approach to semiconductor companies.

“When you have masses of data, and you know what the outcome is but have no clear idea of what the causality is, (deep learning) can bring a complex model of causality that is very hard to do with manual methods,” said Rowen, an IEEE fellow who earlier was the CEO of Tensilica Inc.

The magic of deep learning, Rowen said, is that the learning process is highly automated and “doesn’t require a fab expert to look at the particular defect patterns.”

“It really is a rather brute force, naïve method. You don’t really know what the constituent patterns are that lead to these particular failures. But if you have enough examples that relate inputs to outputs, to defects or to failures, then you can use deep learning.”

Juan Rey, senior director of engineering at Mentor Graphics, said Mentor engineers have started investigating deep-learning techniques which could improve models of the lithography process steps, a complex issue that Rey said “is an area where deep neural networks and machine learning seem to be able to help.”

Juan Rey, Mentor Graphics

In the lithography process “we need to create an approximate model of what needs to be analyzed. For example, for photolithography specifically, there is the transition between dark and clear areas, where the slope of intensity for that transition zone plays a very clear role in the physics of the problem being solved. The problem tends to be that the design, the exact formulation, cannot be used in every space, and we are limited by the computational resources. We need to rely on a few discrete measurements, perhaps a few tens of thousands, maybe more, but it still is a discrete data set, and we don’t know if that is enough to cover all the cases when we model the full chip,” he said.

“Where we see an opportunity for deep learning is to try to do an interpretation for that problem, given that an exhaustive analysis is impossible. Using these new types of algorithms, we may be able to move from a problem that is continuous to a problem with a discrete data set.”

Mentor seeks to cooperate with academia and with research consortia such as IMEC. “We want to find the right research projects to sponsor between our research teams and academic teams. We hope that we can get better results with these new types of algorithms, and in the longer term with the new hardware that is being developed,” Rey said.

Many companies are developing specialized processors to run machine-learning algorithms, including non-Von Neumann, asynchronous architectures, which could offer several orders of magnitude less power consumption. “We are paying a lot of attention to the research, and would like to use some of these chips to solve some of the problems that the industry has, problems that are not very well served right now,” Rey said.

While power savings can still be gained with synchronous architectures, Rey said brain-inspired projects such as Qualcomm’s Zeroth processor, or the use of memristors being developed at H-P Labs, may be able to deliver significant power savings. “These are all worth paying attention to. It is my feeling that different architectures may be needed to deal with unstructured data. Otherwise, total power consumption is going through the roof. For unstructured data, these types of problem can be dealt with much better with neuromorphic computers.”

The use of deep learning techniques is moving beyond the biggest players, such as Google, Amazon, and the like. Just as various system integrators package the open source modules of the Hadoop data base technology into a more-secure offering, several system integrators are offering workstations packaged with the appropriate deep-learning tools.

Deep learning has evolved to play a role in speech recognition used in Amazon’s Echo. Source: Amazon

Robert Stober, director of systems engineering at Bright Computing, bundles AI software and tools with hardware based on Nvidia or Intel processors. “Our mission statement is to deploy deep learning packages, infrastructure, and clusters, so there is no more digging around for weeks and weeks by your expensive data scientists,” Stober said.

Deep learning is driving new the need for new types of processors as well as high-speed interconnects. Tim Miller, senior vice president at One Stop Systems, said that training the neural networks used in deep learning is an ideal task for GPUs because they can perform parallel calculations, sharply reducing the training time. However, GPUs often are large and require cooling, which most systems are not equipped to handle.

David Kanter, principal consultant at Real World Technologies, said “as I look at what’s driving the industry, it’s about convolutional neural networks, and using general-purpose hardware to do this is not the most efficient thing.”

However, research efforts focused on using new materials or futuristic architectures may over-complicate the situation for data scientists outside of the research arena. At the International Electron Devices Meeting (IEDM 2017), several research managers discussed using spin torque magnetic (STT-MRAM) technology, or resistive RAMs (ReRAM), to create dense, power-efficient networks of artificial neurons.

While those efforts are worthwhile from a research standpoint, Kanter said “when proving a new technology, you want to minimize the situation, and if you change the software architecture of neural networks, that is asking a lot of programmers, to adopt a different programming method.”

While Nvidia, Intel, and others battle it out at the high end for the processors used in training the neural network, the inference engines which use the results of that training must be less expensive and consume far less power.

Kanter said “today, most inference processing is done on general-purpose CPUs. It does not require a GPU. Most people I know at Google do not use a GPU. Since the (inference processing) workload load looks like the processing of DSP algorithms, it can be done with special-purpose cores from Tensilica (now part of Cadence) or ARC (now part of Synopsys). That is way better than any GPU,” Kanter said.

Rowen was asked if the end-node inference engine will blossom into large volumes. “I would emphatically say, yes, powerful inference engines will be widely deployed” in markets such as imaging, voice processing, language recognition, and modeling.

“There will be some opportunity for stand-alone inference engines, but most IEs will be part of a larger system. Inference doesn’t necessarily need hundreds of square millimeters of silicon. But it will be a major sub-system, widely deployed in a range of SoC platforms,” Rowen said.

Kanter noted that Nvidia has a powerful inference engine processor that has gained traction in the early self-driving cars, and Google has developed an ASIC to process its Tensor deep learning software language.

In many other markets, what is needed are very low power consumption IEs that can be used in security cameras, voice processors, drones, and many other markets. Nvidia CEO Jen Hsung Huang, in a blog post early this year, said that deep learning will spur demand for billions of devices deployed in drones, portable instruments, intelligent cameras, and autonomous vehicles.

“Someday, billions of intelligent devices will take advantage of deep learning to perform seemingly intelligent tasks,” Huang wrote. He envisions a future in which drones will autonomously find an item in a warehouse, for example, while portable medical instruments will use artificial intelligence to diagnose blood samples on-site.

In the long run, that “billions” vision may be correct, Kanter said, adding that the Nvidia CEO, an adept promoter as well as an astute company leader, may be wearing his salesman hat a bit.

“Ten years from now, inference processing will be widespread, and many SoCs will have an inference accelerator on board,” Kanter said.

Vital Control in Fab Materials Supply-Chains – Part 2

Thursday, February 16th, 2017

By Ed Korczynski, Sr. Technical Editor

As detailed in Part 1 of this article published last month by SemiMD, the inaugural Critical Materials Council (CMC) Conference happened May 5-6 in Hillsboro, Oregon. Held just after the yearly private CMC meeting, the public CMC Conference provides a forum for the pre-competitive exchange of information to control the supply-chain of critical materials needed to run high-volume manufacturing (HVM) in IC fabs. The next CMC Conference will happen May 11-12 in Dallas, Texas.

At the end of the 2016 conference, a panel discussion moderated by Ed Korczynski was recorded and transcribed. The following is Part 2 of the conversation between the following industry experts:

  • Jean-Marc Girard, CTO and Director of R&D, Air Liquide Advanced Materials,
  • Jeff Hemphill, Staff Materials R&D Engineer, Intel Corporation,
  • Jonas Sundqvist, Sr. Scientist, Fraunhofer IKTS; and co-chair of ALD Conference, and
  • John Smythe, Distinguished Member of Technical Staff, Micron Technology.

FIGURE 1: 2016 CMC Conference expert panelists (from left to right) John Smyth, Jonas Sundqvist, Jeff Hemphill, and Jean-Marc Girard. (Source: TECHCET CA)

KORCZYNSKI:  We heard from David Thompson [EDITOR’S NOTE:  Director of Process Chemistry, Applied Materials presented on “Agony in New Material Introductions -  Minimizing and Correlating Variabilities”] today on what we must control, and he gave an example of a so-called trace-contaminant that was essential for the process performance of a precursor, where the trace compound helped prevent particles from flaking off chamber walls. Do we need to specify our contaminants?

GIRARD:  Yes. To David’s point this morning, every molecule is different. Some are very tolerant due to the molecular process associated with it, and some are not. I’ll give you an example of a cobalt material that’s been talked about, where it can be run in production at perhaps 95% in terms of assay, provided that one specific contaminant is less than a couple of parts-per-million. So it’s a combination of both, it’s not assay OR a specification of impurities. It’s a matter of specifying the trace components that really matter when you reach the point that the data you gather gives you that understanding, and obviously an assay within control limits.

HEMPHILL:  Talking about whether we’re over-specifying or not, the emphasis is not about putting the right number on known parameters like assay that are obvious to measure, the emphasis is on identifying and understanding what makes up the rest of it and in a sense trying over-specify that. You identify through mass-spectrometry and other techniques that some fraction of a percent is primarily say five different species, it’s finding out how to individually monitor and track and control those as separate parameters. So from a specification point of view what we want is not necessarily the lowest possible numbers, but it’s expanding how many things we’re looking at so that we’re capturing everything that’s there.

KORCZYNSKI:  Is that something that you’re starting to push out to your suppliers?

HEMPHILL:  Yes. It depends on the application we’re talking about, but we go into it with the assumption that just assay will not be enough. Whether a single molecule or a blend of things is supposed to be there, we know that just having those be controlled by specification will not be sufficient. We go under the assumption that we are going to identify what makes up the remaining part of the profile, and those components are going to need to be controlled as well.

KORCZYNSKI:  Is that something that has changed by node? Back when things were simpler say at 45nm and larger, were these aspects of processing that we could safely ignore as ‘noise’ but are now important ‘signals’?

HEMPHILL:  Yes, we certainly didn’t pay as close attention just a couple of generations ago.

KORCZYNSKI:  That seems to lead us to questions about single-sources versus dual-sourcing. There are many good reasons to do both, but not simultaneously. However, it seems that because of all of the challenges we’re heard about over the last day-and-a-half of this conference it creates greater burden on the suppliers, and for critical materials the fabs are moving toward more single-sourcing over time.

SMYTHE:  I think that it comes down to more of a concern over geographic risk. I’ll buy from one entity if that entity has more than one geographic location for the supply, so that I’m not exposed to a single ‘Act of God’ or a ‘random statistical occurrence of global warming.’ So for example I  need to ask if a supplier has a place in the US and a place in France that makes the same thing, so that if something bad happens in one location it can still be sourced? Or do you have an alternate-supply agreement that if you can’t supply it you have an agreement with Company-X to supply it so that you still have control? You can’t come to a Micron and say we want to make sure that we get at minimum 25% no matter what, because what typically happens with second-sourcing is Company-A gets 75% of the business while Company-B gets 25%. There are a lot of reasons that that doesn’t work so well, so people may have an impression that there’s a movement toward single-source but it’s ‘single flexible-source.’

HEMPHILL:  There are a lot of benefits of dual- or multiple-sourcing. The commercial benefits of competition can be positive and we’re for it when it works. The risk is that as things are progressing and we’re getting more sensitive to differences in materials it’s getting harder to maintain that. We have seen situations where historically we were successful with dual-sourcing a raw material coming from two different suppliers or even a single supplier using two different manufacturing lines and everything was fine and qualified and we could alternate sources invisibly. However, as our sensitivity has grown over time we can start to detect differences.

So the concept of being ‘copy-exactly’ that we use in our factories, we really need production lines to do that, and if we’re talking about two different companies producing the same material then we’re not going to get them to be copy-exactly. When that results in enough of a variation in the material that we can detect it in the factory then we cannot rely upon two sources. Our preference would be one company that maintains multiple production sites that are designed to be exactly the same, then we have a high degree of confidence that they will be able to produce the same material.

FIGURE 2: Jean-Marc Girard, Distinguished Member of Technical Staff of Micron Technology, provided the supplier perspective. (Source: SEMI)

GIRARD:  I can give you a supplier perspective on that. We are seeing very different policies from different customers, to the point that we’re seeing an increase in the number of customers doing single-sourcing with us, provided we can show the ability to maintain business continuity in case of a problem. I think that the industry became mature after the tragic earthquake and tsunami in Japan in 2011 with greater understanding of what business continuity means. We have the same discussions with our own suppliers, who may say that they have a dedicated reactor for a certain product with another backup reactor with a certain capacity on the same site, and we ask what happens if the plant goes on strike or there’s a fire there?

A situation where you might think the supply was stable involved silane in the United States. There are two large silane plants in the United States that are very far apart from each other and many Asian manufacturers dependent upon them. When the U.S. harbors went on strike for a long time there was no way that material could ship out of the U.S. customers. So, yes there were two plants but in such an event you wouldn’t have global supply. So there is no one way to manage our supply lines and we need to have conversations with our customers to discuss the risks. How much time would it take to rebuild a supply-chain source with someone else? If you can get that sort of constructive discussion going then customers are usually open to single-sourcing. One regional aspect is that Asian customers tend to favor dual-sourcing more, but that can lead to IP problems.

[DISCLOSURE:  Ed Korczynski is co-chair of the CMC Conference, and Marketing Director of TECHCET CA the advisory services firm that administers the Critical Materials Council (CMC).]

—E.K.

Innovations at 7nm to Keep Moore’s Law Alive

Thursday, January 19th, 2017

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By Dave Lammers, Contributing Editor

Despite fears that Moore’s Law improvements are imperiled, the innovations set to come in at the 7nm node this year and next may disprove the naysayers. EUV lithography is likely to gain a toehold at the 7nm node, competing with multi-patterning and, if all goes well, shortening manufacturing cycles. Cobalt may replace tungsten in an effort to reduce resistance-induced delays at the contacts, a major challenge with finFET transistors, experts said.

While the industry did see a slowdown in Moore’s Law cost reductions when double patterning became necessary several years ago, Scotten Jones, who runs a semiconductor consultancy focused on cost analysis, said Intel and the leading foundries are back on track in terms of node-to-node cost improvements.

Speaking at the recent SEMI Industry Strategy Symposium (ISS), Jones said his cost modeling backs up claims made by Intel, GlobalFoundries, and others that their leading-edge processes deliver on die costs. Cost improvements stalled at TSMC for the16nm node due to multi-patterning, Jones said. “That pause at TSMC fooled a lot of people. The reality now may surprise those people who said Moore’s Law was dead. I don’t believe that, and many technologists don’t believe that either,” he said.

As Intel has adopted a roughly 2.5-year cadence for its more-aggressive node scaling, Jones said “the foundries are now neck and neck with Intel on density.” Intel has reached best-ever yield levels with its finFET-based process nodes, and the foundries also report reaching similar yield levels for their FinFET processes. “It is hard, working up the learning curve, but these companies have shown we can get there,” he said.

IC Knowledge cost models show the chip industry is succeeding in scaling density and costs. (Source: Scotten Jones presentation at 2017 SEMI ISS)

TSMC, spurred by its contract with Apple to supply the main iPhone processors, is expected to be first to ship its 7nm products late this year, though its design rules (contacted poly pitch and minimum metal pitch) are somewhat close to Intel’s 10nm node.

While TSMC and GlobalFoundries are expected to start 7nm production using double and quadruple patterning, they may bring in EUV lithography later. TSMC has said publicly it plans to exercise EUV in parallel with 193i manufacturing for the 7nm node. Samsung has put its stake in the ground to use EUV rather than quadruple patterning in 2018 for critical layers of its 7nm process. Jones, president of IC Knowledge LLC, said Intel will have the most aggressive CPP and MPP pitches for its 7nm technology, and is likely to use EUV in 2019-2020 to push its metal pitches to the minimum possible with EUV scanners.

EUV progress at imec

In an interview at the 62nd International Electron Devices Meeting (IEDM) in San Francisco in early December, An Steegen, the senior vice president of process technology at Imec (Leuven, Belgium), said Imec researchers are using an ASML NXE 3300B scanner with 0.3 NA optics and an 80-Watt power supply to pattern about 50 wafers per hour.

“The stability on the tool, the up time, has improved quite a lot, to 55 percent. In the best weeks we go well above 70 percent. That is where we are at today. The next step is a 125-Watt power supply, which should start rolling out in the field, and then 250 Watts.”

Steegen said progress is being made in metal-containing EUV resists, and in development of pellicles “which can withstand hydrogen in the chamber.”

If those challenges can be met, EUV would enable single patterning for vias and several metal layers in the middle of the line (MOL), using cut masks to print the metal line ends. “For six or seven thin wires and vias, at the full (7nm node) 32nm pitch, you can do it with a single exposure by going to EUV. The capability is there,” Steegen said.

TSMC’s 7nm development manager, S.Y. Wu, speaking at IEDM, said quadruple patterning and etch (4P4E) will be required for critical layers until EUV reaches sufficient maturity. “EUV is under development (at TSMC), and we will use 7nm as the test vehicle.”

Huiming Bu was peppered with questions following a presentation of the IBM Alliance 7nm technology at IEDM.

Huiming Bu, who presented the IBM Alliance 7nm paper at IEDM, said “EUV delivers significant depth of field (DoF) improvement” compared with the self-aligned quadruple (SAQP) required for the metal lines with immersion scanners.

A main advantage for EUV compared with multi-patterning is that designs would spend fewer days in the fabs. Speaking at ISS, Gary Patton, the chief technology officer at GlobalFoundries, said EUV could result in 30-day reductions in fab cycle times, compared with multiple patterning with 193nm immersion scanners, based on 1.5 days of cycle time per mask layer.

Moreover, EUV patterns would produce less variation in electrical performance and enable tighter process parameters, Patton said.

Since designers have become accustomed to using several colors to identify multi-patterning layers for the 14nm node, the use of double and quadruple patterning at the 7nm node would not present extraordinary design challenges. Moving from multi-patterning to EUV will be largely transparent to design teams as foundries move from multi-patterning to EUV for critical layers.

Interconnect resistance challenges

As interconnects scale and become more narrow, signals can slow down as electrons get caught up in the metal grain boundaries. Jones estimates that as much as 85 percent of parasitic capacitance is in the contacts.

For the main interconnects, nearly two decades ago, the industry began a switch from aluminum to copper. Tungsten has been used for the contacts, vias, and other metal lines near the transistor, partly out of concerns that copper atoms would “poison” the nearby transistors.

Tungsten worked well, partly because the bi-level liner – tantalum nitride at the interface with the inter-level dielectric (ILD) and tantalum at the metal lines – was successful at protecting against electromigration. The TaN-Ta liner is needed because the fluorine-based CVD processes can attack the silicon. For tungsten contacts, Ti serves to getter oxygen, and TiN – which has high resistance — serves as an oxygen and fluorine barrier.

However, as contacts and MOL lines shrunk, the thickness of the liner began to equal the tungsten metal thicknesses.

Dan Edelstein, an IBM fellow who led development of IBM’s industry-leading copper interconnect process, said a “pinch point” has developed for FinFETs at the point where contacts meet the middle-of-the-line (MOL) interconnects.

“With cobalt, there is no fluorine in the deposition process. There is a little bit of barrier, which can be either electroplated or deposited by CVD, and which can be polished by CMP. Cobalt is fairly inert; it is a known fab-friendly metal,” Edelstein said, due to its longstanding use as a silicide material.

As the industry evaluated cobalt, Edelstein said researchers have found that cobalt “doesn’t present a risk to the device. People have been dropping it in, and while there are still some bugs that need to be worked out, it is not that hard to do. And it gives a big change in performance,” he said.

Annealing advantages to Cobalt

Contacts are a “pinch point” and the industry may switch to cobalt (Source: Applied Materials)

An Applied Materials senior director, Mike Chudzik, writing on the company’s blog, said the annealing step during contact formation also favors cobalt: “It’s not just the deposition step for the bulk fill involved – there is annealing as well. Co has a higher thermal budget making it possible to anneal, which provides a superior, less granular fill with no seams and thus lowers overall resistance and improves yield,” Chudzik explained.

Increasing the volume of material in the contact and getting more current through is critical at the 7nm node. “Pretty much every chipmaker is working aggressively to alleviate this issue. They understand if it’s not resolved then it won’t matter what else is done with the device to try and boost performance,” Chudzik said.

Prof. Koike strikes again

Innovations underway at a Japanese university aim to provide a liner between the cobalt contact fill material and the adjacent materials. At a Sunday short course preceding the IEDM, Reza Arghavani of Lam Research said that by creating an alloy of cobalt and approximately 10 percent titanium, “magical things happen” at the interfaces for the contact, M0 and M1 layers.

The idea for adding titanium arose from Prof. Junichi Koike at Tohoku University, the materials scientist who earlier developed a manganese-copper solution for improved copper interconnects. For contacts and MOL, the Co-Ti liner prevents diffusion into the spacer oxide, Arghavani said. “There is no (resistance) penalty for the liner, and it is thermally stable, up to 400 to 500 degrees C. It is a very promising material, and we are working on it. W (tungsten) is being pushed as far as it can go, but cobalt is being actively pursued,” he said.

Stressor changes ahead

Presentations at the 2016 IEDM by the IBM Alliance (IBM, GlobalFoundries, and Samsung) described the use of a stress relaxed buffer (SRB) layer to induce stress, but that technique requires solutions for the defects introduced in the silicon layer above it. As a result of that learning process, SRB stress techniques may not come into the industry until the 5 nm node, or a second-generation 7nm node.

Technology analyst Dick James, based in Ottawa, said over the past decade companies have pushed silicon-germanium stressors for the PFET transistors about as far as practical.

“The stress mechanisms have changed since Intel started using SiGe at the 90nm node. Now, companies are a bit mysterious, and nobody is saying what they are doing. They can’t do tensile nitride anymore at the NFET; there is precious little room to put linear stress into the channel,” he said.

The SRB technique, James said, is “viable, but it depends on controlling the defects.” He noted that Samsung researchers presented work on defects at the IEDM in December. “That was clearly a research paper, and adding an SRB in production volumes is different than doing it in an R&D lab.”

James noted that scaling by itself helps maintain stress levels, even as the space for the stressor atoms becomes smaller. “If companies shorten the gate length and keep the same stress as before, the stress per nanometer at least maintains itself.”

Huiming Bu, the IBM researcher, was optimistic, saying that the IBM Alliance work succeeded at adding both compressive and tensile strain. The SRB/SSRW approach used by the IBM Alliance was “able to preserve a majority – 75 percent – of the stress on the substrate.”

Jones, the IC Knowledge analyst, said another area of intense interest in research is high-mobility channels, including the use of SiGe channel materials in the PMOS FinFETS.

He also noted that for the NMOS finFETs, “introducing tensile stress in fins is very challenging, with lots of integration issues.” Jones said using an SRB layer is a promising path, but added: “My point here is: Will it be implemented at 7 nm? My guess is no.”

Putting it in a package

Steegen said innovation is increasingly being done by the system vendors, as they figure out how to combine different ICs in new types of packages that improve overall performance.

System companies, faced with rising costs for leading-edge silicon, are figuring out “how to add functionality, by using packaging, SOC partitioning and then putting them together in the package to deliver the logic, cache, and IOs with the right tradeoffs,” she said.

Process Control Deals with Big Data, Busy Engineers

Tuesday, November 22nd, 2016

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By Dave Lammers, Contributing Editor

Turning data into insights that will improve fab productivity is one of the semiconductor industry’s biggest opportunities, one that experts say requires a delicate mix between automation and human expertise.

A year ago, after the 2015 Advanced Process Control (APC) conference in Austin, attendees said one of their challenges was that it takes too long to create the fault detection and classification (FDC) models that alert engineers when something is amiss in a process step.

“The industry listened,” said Brad van Eck, APC conference co-chairman. Participants at the 2016 APC in Phoenix heard progress reports from device makers as diverse as Intel, Qorvo, Seagate, and TSMC, as well as from key APC software vendors including Applied Materials, Bistel, and others.

Steve Chadwick, principal engineer for manufacturing IT at Intel, described the challenge in a keynote address. IC manufacturers which have spent billions of dollars on semiconductor equipment are seeking new ways to maximize their investments.

Steve Chadwick

“We all want to increase our quality, make the product in the best time, get the most good die out, and all of that. Time to market can be a game changer. That is universal to the manufacturing space,” Chadwick said.

“Every time we have a new generation of processor, we double the data size. Roughly a gigabyte of information is collected on every wafer, and we sort thousands of wafers a day,” Chadwick said. The result is petabytes of data which needs to be stored, analyzed, and turned into actionable “wisdom.”

Intel has invested in data centers located close their factories, making sure they have the processing power to handle data coming in from roughly 5 billion sensor data points collected each day at a single Intel factory.

“We have to take all of this raw data that we have in a data store and apply some kind of business logic to it. We boil it down to ‘wisdom,’ telling someone something they didn’t know beforehand.”

In a sense, technology is catching up, as Hadoop and several other data search engines are adopted to big data. Also, faster processors allow servers to analyze problems in 15 seconds or less, compared to several hours a few years ago.

Where all of this gets interesting is in figuring out how to relate to busy engineers who don’t want to be bothered with problems that don’t directly concern them. Chadwick detailed the notification problem at Intel fabs, particularly as engineers use smart phones and tablets to receive alarms. “Engineers are busy, and so you only tell them something they need to know. Sometimes engineers will say, ‘Hey, Steve, you just notified my phone of 500 things that I can’t do anything about. Can you cut it out?’”

Notification must be prioritized, and the best option in many cases is to avoid notifiying a person at all, instead sending a notification to an expert system. If that is not an option, the notification has to be tailored to the device the engineer is using. Intel is moving quickly to HTML 5-based data due largely to its portability across multiple devices, he added.

With more than half a million ad hoc jobs per week, Intel’s approach is to keep data and analysis close to the factory, processing whenever possible in the local geography. Instead of shipping data to a distant data center for analysis, the normal procedure is to ship the small analysis code to a very large data set.

False positives decried

Fault detection and classification (FDC) models are difficult to create and oftentimes overly sensitive, resulting in false alarms. These widely used, manually created FDC models can take two weeks or longer to set up. While they take advantage of subject-matter-expert (SME) knowledge and are easy to understand, tool limits tend to be costly to set up and manage, with a high level of false positives and missed alarms.

An Applied Materials presentation — by Parris Hawkins, James Moyne, Jimmy Iskandar, Brad Schulze, and Mike Armacost – detailed work that Applied is doing in cooperation with process control researchers at the University of Cincinnati. The goal is to develop next-generation FDC that leverages Big Data, prediction analytics, and expert engineers to combine automated model development with inputs from human experts.

Fully automated solutions are plagued with significant false positives/negatives, and are “generally not very useful,” said Hawkins. By incorporating metrology and equipment health data, a form of “supervised” model creation can result in more accurate process controls, he said.

The model creation effort first determines which sensors and trace features are relevant, and then optimizes the tool limits and other parameters. The goal is to find the optimum between too-wide limits that fail to alert when faults are existent, and overly tight limits which set off false alarms too often.

Next-generation FDC would leverage Big Data and human expertise. (Source: Applied Materials presentation at APC 2016).

Full-trace FDC

BISTel has developed an approach called Dynamic Full Trace FDC. Tom Ho, president of BISTel USA, presented the work in conjunction with Qorvo engineers, where a beta version of the software is being used.

Tom Ho

Ho said Dynamic Full Trace FDC starts with the notion that the key to manufacturing is repeatability, and in a stable manufacturing environment “anything that differs, isn’t routine, it is an indication of a mis-process and should not be repeatable. Taking that concept, then why not compare a wafer to everything that is supposed to repeat. Based on that, in an individual wafer process, the neighboring wafer becomes the model.”

The full-trace FDC approach has a limited objective: to make an assessment whether the process is good or bad. It doesn’t recommend adjustments, as a run-to-run tool might.

The amount of data involved is small, because it is confined to that unique process recipe. And because the neighboring trace is the model, there is no need for the time-consuming model creation mentioned so often at APC 2016. Compute power can be limited to a personal computer for an individual tool.

Ho took the example of an etch process that might have five recipe steps, starting with pumping down the chamber to the end point where the plasma is turned off. Dynamic full-trace FDC assumes that most wafers will receive a good etch process, and it monitors the full trace to cover the entire process.

“There is no need for a model, because the model is your neighboring trace,” he said. “It definitely saves money in multiple ways. With the rollout of traditional FDC, each tool type can take a few weeks to set up the model and make sure it is running correctly. For multiple tool types that can take a few months. And model maintenance is another big job,” he said.

For the most part, the dynamic full-trace software runs on top of the Bistel FDC platform, though it could be used with another FDC vendor “if the customer has access to the raw trace data,” he said.

Air-Gaps for FinFETs Shown at IEDM

Friday, October 28th, 2016

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By Ed Korczynski, Sr. Technical Editor

Researchers from IBM and Globalfoundries will report on the first use of “air-gaps” as part of the dielectric insulation around active gates of “10nm-node” finFETs at the upcoming International Electron Devices Meeting (IEDM) of the IEEE (ieee-iedm.org). Happening in San Francisco in early December, IEDM 2016 will again provide a forum for the world’s leading R&D teams to show off their latest-greatest devices, including 7nm-node finFETs by IBM/Globalfoundries/Samsung and by TSMC. Air-gaps reduce the dielectric capacitance that slows down ICs, so their integration into transistor structures leads to faster logic chips.

History of Airgaps – ILD and IPD

As this editor recently covered at SemiMD, in 1998, Ben Shieh—then a researcher at Stanford University and now a foundry interface for Apple Corp.—first published (Shieh, Saraswat & McVittie. IEEE Electron Dev. Lett., January 1998) on the use of controlled pitch design combined with CVD dielectrics to form “pinched-off keyholes” in cross-sections of inter-layer dielectrics (ILD).

In 2007, IBM researchers showed a way to use sacrificial dielectric layers as part of a subtractive process that allows air-gaps to be integrated into any existing dielectric structure. In an interview with this editor at that time, IBM Fellow Dan Edelstein explained, “we use lithography to etch a narrow channel down so it will cap off, then deliberated damage the dielectric and etch so it looks like a balloon. We get a big gap with a drop in capacitance and then a small slot that gets pinched off.

Intel presented on their integration of air-gaps into on-chip interconnects at IITC in 2010 but delayed use until the company’s 14nm-node reached production in 2014. 2D-NAND fabs have been using air-gaps as part of the inter-poly dielectric (IPD) for many years, so there is precedent for integration near the gate-stack.

Airgaps for finFETs

Now researchers from IBM and Globalfoundries will report in (IEDM Paper #17.1, “Air Spacer for 10nm FinFET CMOS and Beyond,” K. Cheng et al) on the first air-gaps used at the transistor level in logic. Figure 1 shows that for these “10nm-node” finFETs the dielectric spacing—including the air-gap and both sides of the dielectric liner—is about 10 nm. The liner needs to be ~2nm thin so that ~1nm of ultra-low-k sacrificial dielectric remains on either side of the ~5nm air-gap.

Fig.1: Schematic of partial air-gaps only above fin tops using dielectric liners to protect gate stacks during air-gap formation for 10nm finFET CMOS and beyond. (source: IEDM 2016, Paper#17.1, Fig.12)

These air-gaps reduced capacitance at the transistor level by as much as 25%, and in a ring oscillator test circuit by as much as 15%. The researchers say a partial integration scheme—where the air-gaps are formed only above the tops of fin— minimizes damage to the FinFET, as does the high-selectivity etching process used to fabricate them.

Figure 2 shows a cross-section transmission electron micrograph (TEM) of what can go wrong with etch-back air-gaps when all of the processes are not properly controlled. Because there are inherent process:design interactions needed to form repeatable air-gaps of desired shapes, this integration scheme should be extendable “beyond” the “10-nm node” to finFETs formed at tighter pitches. However, it seems likely that “5nm-node” logic FETs will use arrays of horizontal silicon nano-wires (NW), for which more complex air-gap integration schemes would seem to be needed.

Fig.2: TEM image of FinFET transistor damage—specifically, erosion of the fin and source-drain epitaxy—by improper etch-back of the air-gaps at 10nm dimensions. (source: IEDM 2016, Paper#17.1, Fig.10)

—E.K.

Intel Q1 Revenue, Profit Rise; Chipmaker Will Cut Up to 12,000 Jobs

Wednesday, April 20th, 2016

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By Jeff Dorsch, Contributing Editor

Intel reported net income of $2.0 billion in the first quarter, up 3 percent from a year earlier, while revenue rose 7 percent to $13.7 billion, compared with $12.8 billion one year ago.

The company also announced that it is embarking on an extended restructuring program, eliminating up to 12,000 positions around the world, a reduction in force of about 11 percent, by mid-2017. The cutbacks will include a consolidation of facilities with involuntary and voluntary departures by employees.

“Our first-quarter results tell the story of Intel’s ongoing strategic transformation, which is progressing well and will accelerate in 2016,” Intel CEO Brian Krzanich said in a statement. “We are evolving from a PC company to one that powers the cloud and billions of smart, connected computing devices.”

Intel will focus on its growth businesses – namely, data center, Internet of Things, field-programmable gate arrays, and memory – under the restructuring initiative. The company will realize cost savings of $750 million in 2016 and estimated annual savings of $1.2 billion.

“These actions drive long-term change to further establish Intel as the leader for the smart, connected world,” Krzanich stated. “I am confident that we’ll emerge as a more productive company with broader reach and sharper execution.”

Chief Financial Officer Stacy Smith told analysts the restructuring will make Intel “more agile, more efficient…and more profitable.”

Smith plans to take another post in Intel’s senior management within the next few months. He will be leading sales, manufacturing, and operations once a successor is named as CFO.

Intel said it would consider internal and external candidates for the CFO post.

The company’s second-quarter outlook calls for $13.5 billion in revenue, plus or minus $500 million, with a gross margin percentage of 61 percent. Intel will take a restructuring charge of about $1.2 billion during Q2.

A “weak PC market” in Q1 led to the Client Computing Group posting revenue of $7.5 billion, increasing 2 percent from a year ago yet down 14 percent from the fourth quarter of 2015, Smith said.

The Data Center Group realized Q1 revenue of $4.0 billion, a 9 percent gain from a year earlier. The Internet of Things Group had revenue of $651 million, up 22 percent year-over-year.

Revenue in the Non-Volatile Memory Solutions Group was $557 million, down 6 percent from a year earlier, while the Intel Security Group had Q1 revenue of $537 million, a 12 percent gain from a year ago.

The Programmable Solutions Group, formerly known as Altera (acquired by Intel in late 2015), had $359 million in revenue, not including $99 million in revenue due to acquisition-related adjustments.

Betsy Van Hoes of Wedbush Securities said, “It’s been a long time since there’s been a restructuring of the company. As they forge forward, they need to pare down and invest in the right area. As much as I hate that — it’s terrible for people who are laid off, that — for the investors it’s positive.”

EUV Resists and Stochastic Processes

Friday, March 4th, 2016

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By Ed Korczynski, Sr. Technical Editor

In an exclusive interview with Solid State Technology during SPIE-AL this year, imec Advanced Patterning Department Director Greg McIntyre said, “The big encouraging thing at the conference is the progress on EUV.” The event included a plenary presentation by TSMC Nanopatterning Technology Infrastructure Division Director and SPIE Fellow Anthony Yen on “EUV Lithography: From the Very Beginning to the Eve of Manufacturing.” TSMC is currently learning about EUVL using 10nm- and 7nm-node device test structures, with plans to deploy it for high volume manufacturing (HVM) of contact holes at the 5nm node. Intel researchers confirm that they plan to use EUVL in HVM for the 7nm node.

Recent improvements in EUV source technology— 80W source power had been shown by the end of 2014, 185W by the end of 2015, and 200W has now been shown by ASML—have been enabled by multiple laser pulses tuned to the best produce plasma from tin droplets. TSMC reports that 518 wafers per day were processed by their ASML EUV stepper, and the tool was available ~70% of the time. TSMC shows that a single EUVL process can create 46nm pitch lines/spaces using a complex 2D mask, as is needed for patterning the metal2 layer within multilevel on-chip interconnects.

To improve throughput in HVM, the resist sensitivity to the 13.54nm wavelength radiation of EUV needs to be improved, while the line-width roughness (LWR) specification must be held to low single-digit nm. With a 250W source and 25 mJ/cm2 resist sensitivity an EUV stepper should be able to process ~100 wafer-per-hour (wph), which should allow for affordable use when matched with other lithography technologies.

Researchers from Inpria—the company working on metal-oxide-based EUVL resists—looked at the absorption efficiencies of different resists, and found that the absorption of the metal oxide based resists was ≈ 4 to 5 times higher than that of the Chemically-Amplified Resist (CAR). The Figure shows that higher absorption allows for the use of proportionally thinner resist, which mitigates the issue of line collapse. Resist as thin as 18nm has been patterned over a 70nm thin Spin-On Carbon (SOC) layer without the need for another Bottom Anti-Reflective Coating (BARC). Inpria today can supply 26 mJ/cm2 resist that creates 4.6nm LWR over 140nm Depth of Focus (DoF).

To prevent pattern collapse, the thickness of resist is reduced proportionally to the minimum half-pitch (HP) of lines/spaces. (Source: JSR Micro)

JEIDEC researchers presented their summary of the trade-off between sensitivity and LWR for metal-oxide-based EUV resists:  ultra high sensitivity of 7 mJ/cm2 to pattern 17nm lines with 5.6nm LWR, or low sensitivity of 33 mJ/cm2 to pattern 23nm lines with 3.8nm LWR.

In a keynote presentation, Seong-Sue Kim of Samsung Electronics stated that, “Resist pattern defectivity remains the biggest issue. Metal-oxide resist development needs to be expedited.” The challenge is that defectivity at the nanometer-scale derives from “stochastics,” which means random processes that are not fully predictable.

Stochastics of Nanopatterning

Anna Lio, from Intel’s Portland Technology Development group, stated that the challenges of controlling resist stochastics, “could be the deal breaker.” Intel ran a 7-month test of vias made using EUVL, and found that via critical dimensions (CD), edge-placement-error (EPE), and chain resistances all showed good results compared to 193i. However, there are inherent control issues due to the random nature of phenomena involved in resist patterning:  incident “photons”, absorption, freed electrons, acid generation, acid quenching, protection groups, development processes, etc.

Stochastics for novel chemistries can only be controlled by understanding in detail the sources of variability. From first-principles, EUV resist reactions are not photon-chemistry, but are really radiation-chemistry with many different radiation paths and electrons which can be generated. If every via in an advanced logic IC must work then the failure rate must be on the order of 1 part-per-trillion (ppt), and stochastic variability from non-homogeneous chemistries must be eliminated.

Consider that for a CAR designed for 15mJ/cm2 sensitivity, there will be just:

145 photons/nm2 for 193, and

10 photons/nm2 for EUV.

To improve sensitivity and suppress failures from photon shot-noise, we need to increase resist absorption, and also re-consider chemical amplification mechanisms. “The requirements will be the same for any resist and any chemistry,” reminded Lio. “We need to evaluate all resists at the same exposure levels and at the same rules, and look at different features to show stochastics like in the tails of distributions. Resolution is important but stochastics will rule our world at the dimensions we’re dealing with.”

—E.K.

Many Mixes to Match Litho Apps

Thursday, March 3rd, 2016

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By Ed Korczynski, Sr. Technical Editor

“Mix and Match” has long been a mantra for lithographers in the deep-sub-wavelength era of IC device manufacturing. In general, forming patterns with resolution at minimum pitch as small as 1/4 the wavelength of light can be done using off-axis illumination (OAI) through reticle enhancement techniques (RET) on masks, using optical proximity correction (OPC) perhaps derived from inverse lithography technology (ILT). Lithographers can form 40-45nm wide lines and spaces at the same half-pitch using 193nm light (from ArF lasers) in a single exposure.

Figure 1 shows that application-specific tri-layer photoresists are used to reach the minimum resolution of 193nm-immersion (193i) steppers in a single exposure. Tighter half-pitch features can be created using all manner of multi-patterning processes, including Litho-Etch-Litho-Etch (LELE or LE2) using two masks for a single layer or Self-Aligned Double Patterning (SADP) using sidewall spacers to accomplish pitch-splitting. SADP has been used in high volume manufacturing (HVM) of logic and memory ICs for many years now, and Self-Aligned Quadruple Patterning (SAQP) has been used in HVM by at least one leading memory fab.

Fig.1: Basic tri-layer resist (TLR) technology uses thin Photoresist over silicon-containing Hard-Mask over Spin-On Carbon (SOC), for patterning critical layers of advanced ICs. (Source: Brewer Science)

Next-Generation Lithography (NGL) generally refers to any post-optical technology with at least some unique niche patterning capability of interest to IC fabs:  Extreme Ultra-Violet (EUV), Directed Self-Assembly (DSA), and Nano-Imprint Lithography (NIL). Though proponents of each NGL have dutifully shown capabilities for targeted mask layers for logic or memory, the capabilities of ArF dry and immersion (ArFi) scanners to process >250 wafers/hour with high uptime dominates the economics of HVM lithography.

The world’s leading lithographers gather each year in San Jose, California at SPIE’s Advanced Lithography conference to discuss how to extend optical lithography. So of all the NGL technologies, which will win out in the end?

It is looking most likely that the answer is “all of the above.” EUV and NIL could be used for single layers. For other unique patterning application, ArF/ArFi steppers will be used to create a basic grid/template which will be cut/trimmed using one of the available NGL. Each mask layer in an advanced fab will need application-specific patterning integration, and one of the rare commonalities between all integrated litho modules is the overwhelming need to improve pattern overlay performance.

Naga Chandrasekaran, Micron Corp. vice president of Process R&D, provided a fantastic overview of the patterning requirements for advanced memory chips in a presentation during Nikon’s LithoVision technical symposium held February 21st in San Jose, California prior to the start of SPIE-AL. While resolution improvements are always desired, in the mix-and-match era the greatest challenges involve pattern overlay issues. “In high volume manufacturing, every nanometer variation translates into yield loss, so what is the best overlay that we can deliver as a holistic solution not just considering stepper resolution?” asks Chandrasekaran. “We should talk about cost per nanometer overlay improvement.”

Extreme Ultra-Violet (EUV)

As touted by ASML at SPIE-AL, the brightness and stability and availability of tin-plasma EUV sources continues to improve to 200W in the lab “for one hour, with full dose control,” according to Michael Lercel, ASML’s director of strategic marketing. ASML’s new TWINSCAN NXE:3350B EUVL scanners are now being shipped with 125W power sources, and Intel and Samsung Electronics reported run their EUV power sources at 80W over extended periods.

During Nikon’s LithoVision event, Mark Phillips, Intel Fellow and Director of Lithography Technology Development for Logic, summarized recent progress of EUVL technology:  ~500 wafers-per-day is now standard, and ~1000 wafer-per-day can sometimes happen. However, since grids can be made with ArFi for 1/3 the cost of EUVL even assuming best productivity for the latter, ArFi multi-patterning will continue to be used for most layers. “Resolution is not the only challenge,” reminded Phillips. “Total edge-placement-error in patterning is the biggest challenge to device scaling, and this limit comes before the device physics limit.”

Directed Self-Assembly (DSA)

DSA seems most suited for patterning the periodic 2D arrays used in memory chips such as DRAMs. “Virtual fabrication using directed self-assembly for process optimization in a 14nm DRAM node” was the title of a presentation at SPIE-AL by researchers from Coventor, in which DSA compared favorably to SAQP.

Imec presented electrical results of DSA-formed vias, providing insight on DSA processing variations altering device results. In an exclusive interview with Solid State Technology and SemiMD, imec’s Advanced Patterning Department Director Greg McIntyre reminds us that DSA could save one mask in the patterning of vias which can all be combined into doublets/triplets, since two masks would otherwise be needed to use 193i to do LELE for such a via array. “There have been a lot of patterning tricks developed over the last few years to be able to reduce variability another few nanometers. So all sorts of self-alignments.”

While DSA can be used for shrinking vias that are not doubled/tripled, there are commercially proven spin-on shrink materials that cost much less to use as shown by Kaveri Jain and Scott Light from Micron in their SPIE-AL presentation, “Fundamental characterization of shrink techniques on negative-tone development based dense contact holes.” Chemical shrink processes primarily require control over times, temperatures, and ambients inside a litho track tool to be able repeatably shrink contact hole diameters by 15-25 nm.

Nano-Imprint Litho (NIL)

For advanced IC fab applications, the many different options for NIL technology have been narrowed to just one for IC HVM. The step-and-pattern technology that had been developed and trademarked as “Jet and Flash Imprint Lithography” or “J-FIL” by, has been commercialized for HVM by Canon NanoTechnologies, formerly known as Molecular Imprints. Canon shows improvements in the NIL mask-replication process, since each production mask will need to be replicated from a written master. To use NIL in HVM, mask image placement errors from replication will have to be reduced to ~1nm., while the currently available replication tool is reportedly capable of 2-3nm (3 sigma).

Figure 2 shows normalized costs modeled to produce 15nm half-pitch lines/spaces for different lithography technologies, assuming 125 wph for a single EUV stepper and 60 wph for a cluster of 4 NIL tools. Key to throughput is fast filling of the 26mmx33mm mold nano-cavities by the liquid resist, and proper jetting of resist drops over a thin adhesion layer enables filling times less than 1 second.

Fig.2: Relative estimated costs to pattern 15nm half-pitch lines/spaces for different lithography technologies, assuming 125 wph for a single EUV stepper and 60 wph for a cluster of 4 NIL tools. (Source: Canon)

Researchers from Toshiba and SK Hynix described evaluation results of a long-run defect test of NIL using the Canon FPA-1100 NZ2 pilot production tool, capable of 10 wafers per hour and 8nm overlay, in a presentation at SPIE-AL titled, “NIL defect performance toward high-volume mass production.” The team categorized defects that must be minimized into fundamentally different categories—template, non-filling, separation-related, and pattern collapse—and determined parallel paths to defect reduction to allow for using NIL in HVM of memory chips with <20nm half-pitch features.

—E.K.

What’s the Next-Gen Litho Tech? Maybe All of Them

Thursday, February 25th, 2016

By Jeff Dorsch, Contributing Editor

The annual SPIE Advanced Lithography symposium in San Jose, Calif., hasn’t offered a clear winner in the next-generation lithography race. It’s becoming clearer, however, that 193i immersion and extreme-ultraviolet lithography will co-exist in the future, while directed self-assembly, nanoimprint lithography, and maybe even electron-beam direct-write technology will fit into the picture, too.

At the same time, plasma deposition and etching processes are assuming a greater interdependence with 193i, especially when it comes to multiple patterning, such as self-aligned double patterning, self-aligned quadruple patterning, and self-aligned octuple patterning (yes, there is such a thing!).

“We’ve got to go down to the sub-nanometer level,” Richard Gottscho, Lam Research’s executive vice president of global products, said Monday morning in his plenary presentation at the conference. “We must reduce the variability in multiple patterning,” he added.

Gottscho touted the benefits of atomic level processing in continuing to shrink IC dimensions. Atomic level deposition has been in volume production for a decade or more, he noted, and atomic level etching is emerging as an increasingly useful technology.

When it comes to EUV, “it’s a matter of when, not if,” the Lam executive commented. “EUV will be complementary with 193i.”

Anthony Yen, director of nanopatterning technology in the Infrastructure Division of Taiwan Semiconductor Manufacturing, followed Gottscho in the plenary session. “The fat lady hasn’t sung yet, but she’s on the stage,” he said of EUV.

Harry Levinson, senior director of GlobalFoundries, gave the opening plenary presentation, with the topic of “Evolution in the Concentration of Activities in Lithography.” He was asked after his presentation, “When is the end?” Levinson replied, “We’re definitely not going to get sub-atomic.”

With that limit in mind, dozens of papers were presented this week on what may happen before the semiconductor industry hits the sub-atomic wall.

There were seven conferences within the symposium, on specific subjects, along with a day of classes, an interactive poster session, and a two-day exhibition.

The Alternative Lithographic Technologies conference was heavy on directed self-assembly and nanoimprint lithography papers, while also offering glimpses at patterning with tilted ion implantation and multiphoton laser ablation lithography.

“Patterning is the battleground,” said David Fried, Coventor’s chief technology officer, semiconductor, in an interview at the SPIE conference. He described directed self-assembly as “an enabler for optical lithography.”

Mattan Kamon of Coventor presented a paper on Wednesday afternoon on “Virtual fabrication using directed self-assembly for process optimization in a 14nm DRAM node.”

DSA could be used in conjunction with SAQP or LELELELE, according to Fried. While some lithography experts remain leery or skeptical about using DSA in high-volume manufacturing, the Coventor CTO is a proponent of the technology’s potential.

“Unit process models in DSA are not far-fetched,” he said. “I think they’re pretty close.  The challenges of EUV are well understood. DSA challenges are a little less clear. There’s no ‘one solution fits all’ with DSA.” Fried added, “There are places where DSA can still win.”

Franklin Kalk, executive vice president of technology for Toppan Photomasks, is open to the idea of DSA and imprint lithography joining EUV and immersion in the lithography mix. “It will be some combination,” he said in an interview, while adding, “It’s a dog’s breakfast of technologies. Don’t ever count anything out.”

Richard Wise, Lam’s technical managing director in the company’s Patterning, Global Products Groups CTO Office, said EUV, when ready, will likely be complementary with multipatterning for 7 nanometer.

Self-aligning quadruple patterning, for example, was once considered “insanity” in the industry, yet it is a proven production technology now, he said.

While EUV technology is “very focused on one company,” ASML Holding, there is a consensus at SPIE that EUV’s moment is at hand, Wise said. Intel’s endorsement of the technology and dedication to advancing it speaks volumes of EUV’s potential, he asserted.

“Lam’s always excelled in lot-to-lot control,” an area of significant concern, Wise said, especially with all of this week’s talk about process variability.

What will be the final verdict on the future of lithography technology? Stay tuned.

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