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Posts Tagged ‘SRAM’

TSMC Certifies Mentor Graphics Tools for Early Design Start in TSMC’s 10nm FinFET Technology

Monday, April 6th, 2015

Mentor Graphics Corp. (NASDAQ: MENT) announced that TSMC and Mentor Graphics have reached the first milestone of their collaboration on 10nm EDA certification. Calibre® physical verification and design for manufacturing (DFM) platform, and the Analog FastSPICE™ (AFS™) Circuit Verification Platform, including AFS Mega, are certified by TSMC based on the most current version of 10nm design rules and SPICE models.  New tool feature enhancement based on 10nm process requirements has been made in Olympus-SoC™ digital design platform with TSMC validation, and certification of full chip integration is actively on-going. In addition to 10nm, Mentor has also completed 16FF+ version 1.0 certification of the Calibre, Olympus-SoC and AFS platforms. These certifications provide designers with the earliest access to signoff technology optimized for TSMC’s most advanced process nodes, with improved performance and accuracy.

“The long-term partnership we have with Mentor Graphics enables us to work closely from the earliest phases of technology development so we can have production ready design kits and software available for our customers concurrently with the announcement of new process offerings,” said Suk Lee, TSMC Senior Director, Design Infrastructure Marketing Division. “Mentor’s design solutions have successfully met the accuracy and compatibility requirements for TSMC 10nm FinFET technology, so customers can initiate their designs with accurate verification solutions.”

The Analog FastSPICE Platform provides fast circuit verification for nanometer analog, RF, mixed-signal, memory, and custom digital circuits. For large circuits the AFS Platform also delivers high capacity and fast mixed-signal simulation. For embedded SRAM and other array-based circuits, AFS Mega delivers highly accurate simulation results.

As circuit reliability remains a focus, Mentor and TSMC have enhanced the Calibre PERC™ product offering in 10nm to ensure that design and IP development teams have robust verification solutions for identifying sources of electrical error. Additionally, the Calibre xACT™ extraction suite includes updated models to deliver more accurate results to fulfill tighter accuracy requirements of 10nm.

For TSMC’s 16FF+ 1.0 Calibre design kit release, the Calibre team has worked with TSMC to speed up DRC performance by 30% on average. In addition, TSMC and Mentor released new filling use models that will improve first-pass fill runs, making ECO changes easier and faster. The new fill methodology will also help ensure consistent cycle times during post fill verification.

“Because Mentor and TSMC work together from the earliest stages of design rule development for a new process node, we learn what the new design and verification challenges are right along with TSMC.” said Joseph Sawicki, vice president and general manager of the Design to Silicon division at Mentor Graphics. “This gives us the ability to have the most advanced capabilities in place for ecosystem early adopters, and to continue to optimize performance as the new process moves to full production status.”

Blog review December 16, 2014

Tuesday, December 16th, 2014

Maybe, just maybe, ASML Holding N.V. (ASML) has made the near-impossible a reality by creating a cost-effective Extreme Ultra-Violet (EUV @ ~13.5nm wavelength) all-reflective lithographic tool. The company has announced that Taiwan Semiconductor Manufacturing Company Ltd. (TSMC) has ordered two NXE:3350B EUV systems for delivery in 2015 with the intention to use those systems in production. In addition, two NXE:3300B systems already delivered to TSMC will be upgraded to NXE:3350B performance. While costs and throughputs are conspicuously not-mentioned, this is still an important step for the industry.

The good and the great of the electron device world will make their usual pilgrimage to San Francisco for the 2014 IEEE International Electron Devices Meeting. Dick James of Chipworks writes that it’s the conference where companies strut their technology, and post some of the research that may make it into real product in the next few years.

The 4th Annual Global Interposer Technology Workshop at GaTech gathered 200 attendees from 11 countries to discuss the status of interposer technology. It has become the one meeting where you can find all the key interposer layers including those representing glass, laminate and silicon, blogs Phil Garrou.

Sharon C. Glotzer and Nicholas A. Kotov are both researchers at the University of Michigan who were just awarded a MRS Medal at the Materials Research Society (MRS) Fall Meeting in San Francisco for their work on “Integration of Computation and Experiment for Discovery and Design of Nanoparticle Self-Assembly.”

In order to keep pace with Moore’s Law, semiconductor market leaders have had to adopt increasingly challenging technology roadmaps, which are leading to new demands on electronic materials (EM) product quality for leading-edge chip manufacturing. Dr. Atul Athalye, Head of Technology, Linde Electronics, discusses the challenges.

ST further accelerates its FD-SOI ROs* by 2ps/stage, and reduces SRAM’s VMIN by an extra 70mV. IBM shows an apple-to-apple comparison of 10nm FinFETs on Bulk and SOI. AIST improves the energy efficiency of its FPGA by more than 10X and Nikon shows 2 wafers can be bonded with an overlay accuracy better than 250nm. Adele Hars reports.

Does your design’s interconnect have high enough wire width to withstand ESD? Frank Feng of Mentor Graphics writes in his blog that although applying DRC to check for ESD protection has been in use for a while, designers still struggle to perform this check, because a pure DRC approach can’t identify the direction of an electrical current flow, which means the check can’t directly differentiate the width or length of a wire polygon against a current flow.

At the recent IMAPS conference, Samsung electro-mechanics compared their Plated Mold Via Technology (PMV) to the well known Amkor Through Mold Via  (TMV) technology. The two process flows are compared. Phil Garrou reports.

Foundry, EDA partnership eases move to advanced process nodes

Monday, September 15th, 2014

By Dr. Lianfeng Yang, Vice President of Marketing, ProPlus Design Solutions, Inc., San Jose, Calif.

Partnerships are the lifeblood of the semiconductor industry, and when moving to new advanced nodes, industry trends show closer partnerships and deeper collaborations between foundries, EDA vendors and design companies to ease the transition.

It’s fitting, then, for me to pay homage in this blog post to a successful and long-term partnership between a foundry and an EDA tool supplier.

A leading semiconductor foundry and an EDA vendor with design-for-yield (DFY) solutions have enjoyed a long-term partnership. Recently, they worked together to leverage DFY technologies for process technology development and design flow enhancement. The goals were to improve SRAM yield and provide faster turnaround of a new process platform development.

The foundry used the EDA firm’s high-sigma DFY solution to optimize its SRAM yield for 28nm processes development. Early this year, it announced 28nm readiness for multi-project wafer (MPW) customers. One of the reasons it was able to release the 28nm process with acceptable SRAM yield in a short time was due to a new methodology for SRAM yield ramping that deployed a DFY engine.

During advanced technology development, the time spent on SRAM yield ramping is significant because statistical process variation, particularly local variation between two identical neighboring devices sometimes called mismatch, limits SRAM parametric yield. The impact of local process variation increases when moving to smaller CMOS technology nodes.

In the meantime, supply voltage is reduced, so operating regions are smaller. The difficulty achieving high yield for SRAM is greater because smaller nodes require higher SRAM density. Such challenges require very high sigma robustness or high SRAM bitcell yield. Statistically, the analysis for the SRAM bitcell at 28nm needs to be at around 6 σ, while FinFET technology at 16/14nm sets even higher sigma requirements for SRAM bitcell yield.

During technology development, foundry engineers improve the process to solve defect-related yield issues first. Design-for-manufacturing methodologies can be used to eliminate some systematic process variations. However, many random process variations, such as random dopant fluctuations (RDF), line edge and width roughness (LER, LWR), are fundamental limiting factors for parametric yield particular to SRAM.

Traditionally, foundry engineers rely on experience and know-how from previous node development efforts to analyze and decide how to run different process splits for different process improvement scenarios to optimize SRAM yield. These efforts are often time-consuming and less effective at advanced nodes like 28nm because the optimization margin is much smaller.

The fab’s new SRAM yielding flow used a high sigma statistical simulator as the core engine. It provided fast and accurate 3-7+σ yield prediction and optimization functions for memory, logic and analog circuit designs. During process development, the tool proved its technology advantages in both accuracy and performance, and was validated by silicon in several rounds of tape outs throughout the development process. It required no additional tuning on technology or special settings on the tool usage, so even process engineers who are not familiar with EDA tools could run them to get reliable results to guide their process tuning for SRAM yield improvement.

The flow was able to predict SRAM yield for different process and operating conditions. It simulated SRAM yield improvement trends and provided process improvement direction and guidelines within hours. A methodology such as this becomes necessary for advanced nodes where the remaining optimization margin is small. A simulation-based methodology can run through all possible combinations that process engineers want to explore, providing better yield results and faster yield ramping. Comparatively, the traditional way of exploration based on experiences and running large amount of process splits is limited and may not yield optimum results. It also is time consuming as the engineer would need to wait for tape out results then run another set of trials that could consume months.

The flow saved months ramping up SRAM yield for the 28nm process node. It reduced iteration time and saved wafer cost. Process engineers now only need to fabricate selective wafers to validate simulation results. They know which direction is optimal and have guidelines to run process splits that will help them identify the best conditions and converge on the best yield. They gained greater certainty as they saw more simulation-to-silicon correlation data as the project progressed.

A well-established methodology and flow brings value to process engineers because they can rely on DFY high sigma simulations to lay the foundation for their process improvement strategies to reach certain SRAM yield targets. They can run selective process splits to verify the results for lower wafer costs, fewer process tuning iterations and faster time to market.

Overall, this is a highly successful and mutually beneficial partnership, and the value of DFY to process technology development, is obvious. The same DFY methodology can be used for memory designers as SRAM yield is their primary target as well. The only difference is it tunes design variables using the same methodology, flow and tool solutions.

It’s easy to see the value of a tight collaboration between the foundry, EDA vendor and design companies and why it will be a trend on top of the “foundry-fabless” business model.

About Dr. Lianfeng Yang

Lianfeng Yang, ProPlus Solutions, Inc.

Dr. Lianfeng Yang currently serves as the Vice President of Marketing at ProPlus Design Solutions, Inc. Prior to co-founding ProPlus, he was a senior product engineer at Cadence Design Systems leading the product engineering and technical support effort for the modeling product line in Asia. Dr. Yang has over 40 publications and holds a Ph.D. degree in Electrical Engineering from the University of Glasgow in the U.K.

The Week in Review: April 4, 2014

Friday, April 4th, 2014

Park Systems this week introduced the Automatic Defect Review AFM for 300mm bare wafers, a fully automated AFM solution that improves throughput of AFM defect review by up to 1,000 percent.

ON Semiconductor announced this week that it will acquire Trusense Imaging, Inc. ON Semiconductor will pay approximately $92 million in cash to acquire Truesense Imaging.

SureCore announced that early testing of its innovative low power SRAM design confirms its simulations that deliver in excess of 50 percent power savings over other SRAM technologies.

Researchers from Georgia Tech, University of Texas at Austin, and the Raytheon Company have developed a thermal interface material able to conduct heat 20 times better than the original polymer. The modified material can reliably operate at temperatures of up to 200 degrees Celsius.

UVOTECH announced the release of the UV Ozone Cleaning solution. The UV Ozone Cleaning process is a photo-sensitized oxidation process in which the contaminant molecules of photo resists, resins, human skin oil, cleaning solvent residues, silicone oils, and flux are excited and/or dissociated by the absorption of short wavelength UV radiation.

FinFET on SOI: Potential Becomes Reality

Thursday, December 5th, 2013

Authors: T. B. Hook, I. Ahsan, A. Kumar, K. McStay, E. Nowak, S. Saroop, C. Schiller, G. Starkey, IBM Semiconductor Research and Development Center

We report here empirical results demonstrating the electrical benefits of SOI-based FinFETs. There are benefits inherent in the elimination of dopant as the means to establish the effective device dimensions.  However, significant compromise is unavoidable when using doping as a means of isolation, as in bulk-based FinFETs.  Accordingly, we use SOI as the base on which to build the FinFET, which not only simplifies the process but enables full realization of the potential of the device.
Fully depleted transistor technologies – both planar and SOI-based FinFET – offer excellent circuit operation for SRAM and DRAM due to the unsurpassed threshold voltage matching associated with the near-absence of doping.   Additionally, good low voltage and stacked-fet circuit operation is realized due to the superior electrostatics associated with thin-body devices.  Hardware data specifically illustrating these features is described below.

Threshold voltage matching and distribution

A significant improvement in threshold voltage mismatch has been well documented, as well as the degradation associated with adding doping to a FinFET.  Less well publicized, however, is the even larger relative benefit to be found in thick-dielectric transistors, such as are used for analog and IO devices, and also in DRAM.

Random dopant fluctuation is not the only mechanism contributing to local threshold voltage mismatch, but it has historically been the largest contributor.  It has been an even larger contributor for thicker dielectrics, as its baleful influence scales directly with dielectric thickness, unlike work function variations for example.   Therefore an even more dramatic improvement in matching is found in thick-dielectric devices, as shown in Figure 1.

Figure 1. Mismatch data as a function of tinv for conventional doped (dotted line) and SOI FinFET (solid line). While the improvement in matching for ‘thin-oxide’ (1.2-1.5nm) is well known, less widely recognized is the even larger advantage obtained with ‘thick-oxide’ (>3nm) devices commonly used in IO and analog applications.

This improvement is important to IO and analog circuit operation and is vital to scaling the DRAM transfer device into the next generations.
In Figure 2 are shown probability plots of the threshold voltage for two DRAM transfer gate transistors and the profound improvement is obvious.   The FinFET version actually has a considerably thicker gate dielectric than the conventional doped device and a shorter gate yet much better matching.  The absence of thickness-driven matching opens up the device design space and enables optimization of the overall design, as well as allowing for the fundamental area scaling needed to move to the next generation.

Figure 2: Threshold voltage matching for DRAM transfer devices. Blue: 32nm generation thick oxide doping-controlled device. Red: 14nm generation thick oxide FinFET device. The FinFET device is shorter and has a thicker dielectric, yet the threshold voltage matching standard deviation is 0.7X that of the conventional planar doped version. This improvement is applicable also to other thick oxide devices, such as are used in IO and analog applications.

SRAM Vmin
One of the most important benefits of improved matching is the much-desired reduction in the minimum operating voltage of the classic 6T SRAM.  While the transistor matching data clearly show an advantage, putting it all together into a quantized FinFET SRAM cell with correct beta and gamma ratios and device centering to actually achieve low Vmin is a larger challenge.
Additionally, there may be other factors present in the scaled-up SRAM array that may not be so evident in the classic Pelgrom analysis from which most matching data are derived, such as some perturbation to line-edge-roughness, or nfet/pfet interactions, or any number of other possibilities.

Our data demonstrate that these concerns are surmountable and that real SOI FinFET SRAMs can operate at very low voltages. Figure 3 shows remarkable results on an SRAM array, with full read and write operation down to 400mV, without any assist circuitry.  This is among the best results ever reported, even among those that utilize boost techniques and in-situ tuning of the devices.

Figure 3: Shmoo plot of 14nm SOI FinFET SRAM array showing a minimum operating voltage of 400mV, with full read and write capability. This result, as good or better than any yet reported, was obtained without benefit of the chip-specific tuning techniques associated with planar fully depleted devices or specialized independent double-gate FinFETs.

Low Voltage Circuit Operation
A considerable improvement in electrostatics associated with the FinFET over conventional doped devices not only enables the necessary gate-length scaling, but simultaneously improves the relative performance at reduced voltage and therefore reduces the power density at a given performance.  While fully-depleted devices should in principle enjoy this advantage, the introduction of non-uniformity such as is involved with the tapered fin profile associated with bulk-based FinFET seriously compromises the output conductance and may obviate these expectations, as shown in Figure 4.

Figure 4: Representative bulk-based and SOI-based fin profiles, and corresponding empirical degradation in electrostatics. The tapered shape of the bulk fin shown results in nonuniform current flow and poorer low-voltage operation and self-gain than the more ideally shaped SOI FinFET.

The fin profile obtainable in SOI-based FinFETs is very nearly ideal and our data show that the low voltage benefits are fully realized in hardware.  The frequencies of a suite of ring oscillator circuits (inverter, NANDs, and NORs) were measured on 14nm SOI-based FinFET hardware as a function of voltage and compared to the modeled expectations.
Figure 5 shows excellent correspondence with expectation, and also shows how the data are far superior to the voltage dependence of conventional planar technology.

Figure 5: Normalized frequency reduction as a function of Vdd for a suite of circuits (NANDs, NORs, and inverters). Near-perfect correspondence of the SOI FinFET data with the compact model is shown. This flatter voltage dependence is highly superior to that typical of doping-controlled planar technology.

Conclusion
Several key elements of the putative advantages of FinFETs over conventional devices have been demonstrated in hardware.  By using SOI-based FinFET technology, the need for doping in the body has been effectively minimized, resulting in excellent matching characteristics in the undoped DRAM transfer device, and truly remarkable minimum operating voltage in the SRAM.  Additionally, the superior voltage dependence and stacked-fet circuit behavior relative to conventional devices has also been demonstrated through measurements of ring oscillators of various sorts.

Monte Carlo Analysis Has Become A Gamble

Monday, October 21st, 2013

Dr. Bruce McGaughy, CTO and SVP of Engineering at ProPlus Design Solutions, Inc. blogs about the wisdom of Monte Carlo analysis when high sigma methods are perhaps better suited to today’s designs.

Years ago, someone overhead a group of us talking about Monte Carlo analysis and thought we were referring to the gambling center of Monaco and not computational algorithms that have become the gold standard for yield prediction. All of us standing by the company water cooler had a good laugh. That someone was forgiven because he was a recent college graduate with a degree in Finance and a new hire. As a fast learner, he quickly came to understand the benefits of Monte Carlo analysis.

I was recently reminded of this scene as the limitations of Monte Carlo analysis approaches are becoming more acute because of capacity. No circuit designer would mistake Monte Carlo analysis for a roulette wheel, though chip design may seem like a game of chance today. We continue to use the Monte Carlo approach for high-dimension integration and failure analysis even as new approaches emerge.

Emerging they are. For example, high sigma methods with proven techniques are becoming more prevalent for the design of airplanes, bridges, financial models, integrated circuits and more. Moreover, high sigma methods also are used for electronic design for various applications and are proving to be accurate by validation in hardware.

New technologies, such as16nm FinFET, add extra design challenges that require high sigma greater than six and closer to 7 sigma, making Monte Carlo simulation even less desirable.

Let’s explore a real-world scenario using a memory design as an example where process variations at advanced technologies become more severe, leading to a greater impact on SRAM yield.

The repetitive structure circuits of an SRAM design means extremely low cell failure rate is necessary to ensure high chip yield. Traditional Monte Carlo analysis is impractical in this application. In fact, it’s nearly impossible to finish the needed sampling because it typically requires millions or even billions of runs.

Conversely, a high sigma method can cut Monte Carlo analysis sampling by orders of magnitude. A one megabyte SRAM would require the yield of a bitline cell to reach as high as 99.999999% in order to achieve a chip yield of 99%. Monte Carlo analysis would need billions of samples. The high sigma method would need mere thousands of samples to achieve the same accuracy, shortening the statistical simulation time and making it possible for designers to do yield analysis for this kind of application.

High sigma methods are able to identify and filter sensitive parameters, and identify failure regions. Results are shared in various outputs and include sigma convergence data, failure rates, and yield data equivalent to Monte Carlo samples.

Monte Carlo analysis has had a good long run for yield prediction, but for many cases it’s become impractical. Emerging high sigma methods improve designer confidence for yield, power, performance and area, shorten the process development cycle and have the potential to save cost. The ultimate validation, of course, is in hardware and production usage. High sigma methods are gaining extensive silicon validation over volume production.

Let’s not gamble with yield prediction and take a more careful look at high sigma methods.

About Bruce McGaughy

Bruce McGaughy, CTO and Senior VP of Engineering at ProPlus Solutions in San Jose, CA.
Bruce McGaughy, CTO and Senior VP of Engineering at ProPlus Solutions in San Jose, CA.

Dr. Bruce McGaughy is chief technology officer and senior vice president of Engineering of ProPlus Design Solutions, Inc. He was most recently the Chief Architect of Simulation Division and Distinguished Engineer at Cadence Design Systems Inc. Dr. McGaughy previously also served as a R&D VP at BTA Technology Inc. and Celestry Design Technology Inc., and later an Engineering Group Director at Cadence Design Systems Inc. Dr. McGaughy holds a Ph.D. degree in EECS from the University of California at Berkeley.