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Posts Tagged ‘model’

Mechanistic Modeling of Silicon ALE for FinFETs

Tuesday, April 25th, 2017


With billions of device features on the most advanced silicon CMOS ICs, the industry needs to be able to precisely etch atomic-scale features without over-etching. Atomic layer etching (ALE), can ideally remove uniform layers of material with consistent thickness in each cycle, and can improve uniformity, reduce damage, increase selectivity, and minimize aspect ratio dependent etching (ARDE) rates. Researchers Chad Huard et al. from the University of Michigan and Lam Research recently published “Atomic layer etching of 3D structures in silicon: Self-limiting and nonideal reactions” in the latest issue of the Journal of Vacuum Science & Technology A ( Proper control of sub-cycle pulse times is the key to preventing gas mixing that can degrade the fidelity of ALE.

The authors modeled non-idealities in the ALE of silicon using Ar/Cl2 plasmas:  passivation using Ar/Cl2 plasma resulting in a single layer of SiClx, followed by Ar-ion bombardment to remove the single passivated layer. Un-surprisingly, they found that ideal ALE requires self-limited processes during both steps. Decoupling passivation and etching allows for several advantages over continuous etching, including more ideal etch profiles, high selectivity, and low plasma-induced damage. Any continuous etching —when either or both process steps are not fully self-limited— can cause ARDE and surface roughness.

The gate etch in a finFET process requires that 3D corners be accurately resolved to maintain a uniform gate length along the height of the fin. In so doing, the roughness of the etch surface and the exact etch depth per cycle (EPC) are not as critical as the ability of ALE to be resistant to ARDE. The Figure shows that the geometry modeled was a periodic array of vertical crystalline silicon fins, each 10nm wide and 42nm high, set at a pitch of 42 nm. For continuous etching (a-c), simulations used a 70/30 mix of Ar/Cl gas and RF bias of 30V. Just before the etch-front touches the underlying SiO2 (a), the profile has tapered away from the trench sidewalls and the etch-front shows some micro-trenching produced by ions (or hot neutrals) specularly reflected from the tapered sidewalls. After a 25% over-etch (b), a significant amount of Si remains in the corners and on the sides of the fins. Even after an over-etch of 100% (c), Si still remains in the corners.

FIGURE CAPTION: Simulated profiles resulting from etching finFET gates with (a)–(c) a continuous etching process, or (d)–(f) an optimized ALE process. Time increases from left to right, and images represent equal over-etch (as a percentage of the time required to expose the bottom SiO2) not equal etch times. Times listed for the ALE process in (d)–(f) represent plasma-on, ignoring any purge or dwell times. (Source: J. Vac. Sci. Technol. A, Vol. 35, No. 3, May/Jun 2017)

In comparison, the ALE process (d-f) shows that after 25% over-etch (e) the bottom SiO2 surface would be almost completely cleared with minimal corner residues, and continuing to 100% over-etch results in little change to the profile. The ALE process times shown here do not include the gas purge and fill times between plasma pulses; to clear the feature using ALE required 200 pulses and assuming 5 seconds of purge time between each pulse results in a total process time of 15–20 min to clear the feature. This is a significant increase in total process time over the continuous etch (2 min).

One conclusion of this ALE modeling is that even small deviations from perfectly self-limited reactions significantly compromise the ideality of the ALE process. For example, having as little as 10 ppm Cl2 residual gas in the chamber during the ion bombardment phase produced non-idealities in the ALE. Introducing any source of continuous chemical etching into the ALE process leads to the onset of ARDE and roughening of the etch front. These trends have significant implications for both the design of specialized ALE chambers, and also for the use of ALE to control uniformity.


Memristor Variants and Models from Knowm

Friday, January 22nd, 2016


By Ed Korczynski, Sr. Technical Editor

Knowm Inc. (, a start-up pioneering next-generation advanced computing architectures and technology, recently announced the availability of two new variations of memristors targeting different neuromorphic applications. The company also announced raw device data available for purchase to help researchers develop and improve memristor models. These new Knowm offerings enable the next step in the R&D of radically new chips for pattern-recognition, machine-learning, and artificial intelligence (AI) in general.

There is general consensus between industry and academia and government that future improvements in computing are now severely limited by the amount of energy it takes to use Von Neumann architectures. Consequently, the US Whitehouse has issued a grand challenge with the Energy-Efficient Computing: from Devices to Architectures (E2CDA) program ( actively soliciting proposals through March 28, 2016.

The Figure shows a schematic cross-section of Knowm’s memristor devices—with Tin (Sn) and Chromium (Cr) metal layers as the new options to tungsten (W)—along with the device I/V curves for each. “They differ in their activation threshold,” explained Knowm CEO and co-founder Alex Nugent in an exclusive interview with Solid State Technology. “As the activation thresholds become smaller you get reduced data retention, but higher cycle endurance. As that threshold increases you have to dissipate more energy per event, and the more energy you dissipate the faster it will burn-out.” Knowm’s two new memristors, as well as the company’s previously announced device, are now available as unpackaged raw dice with masks designed for research probe stations.

Figure: Schematic cross-section of Knowm’s memristor devices using Tin (Sn) or Chromium (Cr) or tungsten (W) metal layers, along with the device I/V curves for each. (Source: Knowm)

Knowm is working on the simultaneous co-optimization of the entire “stack” from memristors to circuit architectures to application-specific algorithms. “The potential of memristors is so huge that we are seeing exponential growth in the literature, a sort of gold rush as engineers race to design new circuits and re-envision old circuits,” commented Knowm CEO and co-founder Alex Nugent. “The problem is that in the race to publish, circuit designers are adopting models that do not adequately describe real devices.” Knowm’s raw data includes AC, DC, pulse response, and retention for different memristors.

Additional memristors are being developed by Knowm’s R&D lab partner Dr. Kris Campbell of  Boise State University (, using different metal layers to achieve different activation thresholds beyond the three shown to date. “She has discovered an algorithm for creating memristors along this dimension,” said Nugent. “From a physics perspective it makes sense that there would be devices with high cycle endurance but reduced data retention.”

“In the future what I image is a single chip with multiple memristors on it. Some will be volatile and very fast, while others will be slow,” continued Nugent. “Just like analog design today uses different capacitors, future neuromophic chips would likely use memristors optimized for different changes in adaptation threshhold. If you think about memristors as fundamental elements—as per Leon Chua (—then it makes sense that we’ll need different memristors.”

The applications spaces for these devices have intrinsically different requirements for speed and retention. For example, to exploit these devices for pattern recognition and/or anomaly detection (keeping track of confidence in making temporal predictions) it seems best to choose relatively high activation thresholds because the number of operations is unlikely to burn-out devices. Conversely, for circuits that constantly solve optimization problems the best memristors would require low burn-out and thus low activation thresholds. However, analog applications are generally problematic because the existing memristors leak current, such that stored values degrade over time.

Knowm is shipping devices today, mostly to university researchers, and has tested thousands of devices itself. The Knowm memristors can be fabricated at <500°C using industry-standard unit-process steps, allowing for eventual integration with silicon CMOS “back-end” metallization layers. While still in early R&D, this technology could provide much of the foundation for post-Moore’s-Law silicon ICs.


3DIC Technology Drivers and Roadmaps

Monday, June 22nd, 2015


By Ed Korczynski, Sr. Technical Editor

After 15 years of targeted R&D, through-silicon via (TSV) formation technology has been established for various applications. Figure 1 shows that there are now detailed roadmaps for different types of 3-dimensional (3D) ICs well established in industry—first-order segmentation based on the wiring-level/partitioning—with all of the unit-processes and integration needed for reliable functionality shown. Using block-to-block integration with 5 micron lines at leading international IC foundries such as GlobalFoundries, systems stacking logic and memory such as the Hybrid Memory Cube (HMC) are now in production.

Fig. 1: Today’s 3D technology landscape segmented by wiring-level, showing cross-sections of typical 2-tier circuit stacks, and indicating planned reductions in contact pitches. (Source: imec)

“There are interposers for high-end complex SOC design with good yield,” informed Eric Beyne, Scientific Director Advanced Packaging & Interconnect for imec in an exclusive interview with Solid State Technology. ““For a systems company, once you’ve made the decision to go 3D there’s no way back,” said Beyne. “If you need high-bandwidth memory, for example, then you’re committed to some sort of 3D. The process is happening today.” Beyne is scheduled to talk about 3D technology driven by 3D application requirements in the imec Technology Forum to be held July 13 in San Francisco.

Adaptation of TSV for stacking of components into a complete functional system is key to high-volume demand. Phil Garrou, packaging technologist and SemiMD blogger, reported from the recent ConFab that Hynix is readying a second generation of high-bandwidth memory (HBM 2) for use in high performance computing (HPC) such as graphics, with products already announced like Pascal from Nvidia and Greenland from AMD.

For a normalized 1 cm2 of silicon area, wide-IO memory needs 1600 signal pins (not counting additional power and ground pins) so several thousand TSV are needed for high-performance stacked DRAM today, while in more advanced memory architectures it could go up by another factor of 10. For wide-IO HVM-2 (or Wide-IO2) the silicon consumed by IO circuitry is maybe 6 cm2 today, such that a 3D stack with shorter vertical connections would eliminate many of the drivers on the chip and would allow scaling of the micro-bumps to perhaps save a total of 4 cm2 in silicon area. 3D stacks provide such trade-offs between design and performance, so the best results are predicted for 3DICs where the partitioning can be re-done at the gate or transistor level. For example, a modern 8-core microprocessor could have over 50% of the silicon area consumed by L3-cache-memory and IO circuitry, and moving from 2D to 3D would reduce total wire-lengths and interconnect power consumptions by >50%.

There are inherent thresholds based on the High:Width ratio (H:W) that determine costs and challenges in process integration of TSV:

-    10:1 ratio is the limit for the use of relatively inexpensive physical vapor deposition (PVD) for the Cu barrier/seed (B/S),

-    20:1 ratio is the limit for the use of atomic-layer deposition (ALD) for B/S and electroless deposition (ELD) for Cu fill with 1.5 x 30 micron vias on the roadmap for the far future,

-    30:1 ratio and greater is unproven as manufacturable, though novel deposition technologies continue to be explored.

TSV Processing Results

The researchers at imec have evaluated different ways of connecting TSV to underlying silicon, and have determined that direct connections to micro-bumps are inherently superior to use of any re-distribution layer (RDL) metal. Consequently, there is renewed effort on scaling of micro-bump pitches to be able to match up with TSV. The standard minimum micro-bump pitch today of 40 micron has been shrunk to 20, and imec is now working on 10 micron with plans to go to 5 micron. While it may not help with TSV connections, an RDL layer may still be needed in the final stack and the Cu metal over-burden from TSV filling has been shown by imec to be sufficiently reproducible to be used as the RDL metal. The silicon surface area covered by TSV today is a few percents not 10s of percents, since the wiring level is global or semi-global.

Regarding the trade-offs between die-to-wafer (D2W) and wafer-to-wafer (W2W) stacking, D2W seems advantageous for most near-term solutions because of easier design and superior yield. D2W design is easier because the top die can be arbitrarily smaller silicon, instead of the identically sized chips needed in W2W stacks. Assuming the same defectivity levels in stacking, D2W yield will almost always be superior to W2W because of the ability to use strictly known-good-die. Still, there are high-density integration concepts out on the horizon that call for W2W stacking. Monolithic 3D (M3D) integration using re-grown active silicon instead of TSV may still be used in the future, but design and yield issues will be at least comparable to those of W2W stacking.

Beyne mentioned that during the recent ECTC 2015, EV Group showed impressive 250nm overlay accuracy on 450mm wafers, proving that W2W alignment at the next wafer size will be sufficient for 3D stacking. Beyne is also excited by the fact the at this year’s ECTC there was, “strong interest in thermo-compression bonding, with 18 papers from leading companies. It’s something that we’ve been working on for many years for die-to-wafer stacking, while people had mistakenly thought that it might be too slow or too expensive.”

Thermal issues for high-performance circuitry remain a potential issue for 3D stacking, particularly when working with finFETs. In 2D transistors the excellent thermal conductivity of the underlying silicon crystal acts like a built-in heat-sink to diffuse heat away from active regions. However, when 3D finFETs protrude from the silicon surface the main path for thermal dissipation is through the metal lines of the local interconnect stack, and so finFETs in general and stacks of finFETs in particular tend to induce more electro-migration (EM) failures in copper interconnects compared to 2D devices built on bulk silicon.

3D Designs and Cost Modeling

At a recent North California Chapter of the American Vacuum Society (NCCAVS) PAG-CMPUG-TFUG Joint Users Group Meeting discussing 3D chip technology held at Semi Global Headquarters in San Jose, Jun-Ho Choy of Mentor Graphics Corp. presented on “Electromigration Simulation Flow For Chip-Scale Parametric Failure Analysis.” Figure 2 shows the results from use of a physics-based model for temperature- and residual-stress-aware void nucleation and growth. Mentor has identified new failure mechanisms in TSV that are based on coefficient of thermal expansion (CTE) mismatch stresses. Large stresses can develop in lines near TSV during subsequent thermal processing, and the stress levels are layout dependent. In the worst cases the combined total stress can exceed the critical level required for void nucleation before any electrical stressing is applied. During electrical stress, EM voids were observed to initially nucleate under the TSV centers at the landing-pad interfaces even though these are the locations of minimal current-crowding, which requires proper modeling of CTE-mismatch induced stresses to explain.

Fig. 2: Calibration of an Electronic Design Automation (EDA) tool allows for accurate prediction of transistor performance depending on distance from a TSV. (Source: Mentor Graphics)

Planned for July 16, 2015 at SEMICON West in San Francisco, a presentation on “3DIC Technology Past, Present and Future” will be part of one of the side Semiconductor Technology Sessions (STS). Ramakanth Alapati, Director of Packaging Strategy and Marketing, GLOBALFOUNDRIES, will discuss the underlying economic, supply chain and technology factors that will drive productization of 3DIC technology as we know it today. Key to understanding the dynamic of technology adaptation is using performance/$ as a metric.