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FLCC Seminar Opportunities and Themes for The Next Four Years

Discover seminar opportunities and themes for the next four years in mechanical engineering, electrical engineering, and computer sciences at the University of California, Berkeley. Explore insights from leading industry partners and faculty members.

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FLCC Seminar Opportunities and Themes for The Next Four Years

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  1. FLCC SeminarOpportunities and Themes forThe Next Four Years Professor Kameshwar Poolla Mechanical Engineering Electrical Engineering & Comp Sciences University of California, Berkeley 510-520-1150 DFM

  2. Inputs From … • Cadence, Mentor, Synopsys, KT, Marvell, AMD • FLCC Faculty • Will get more inputs in coming 2 months from • OEMS AMAT, LAM, Nikon, TEL, ASML… • Vertically integrated users Intel, IBM, … • Fabless Design houses Qualcomm, … • DfM/EDA Magma, Luminescence, … • Special thanks to: Luigi Capodieci, Roawen Chen, Nick Cobb, Nickhil Jakadtar, Eric Minami, Dipu Pramanik, Frank Schellenberg, Bhanwar Singh, John Stirnirman, Albert Wu DFM

  3. The Next Four Years • Jan 2008 – Jan 2012 • Why do this? • Build on 8 years of success • Provide value to industrial partners • UC Discovery is a great leveraged funding source • The new proposal • Submit in October 07 • Need a brand new four proposal • Not just an extension • Evolutionary • Not a complete change • Must have continuity and stability for our University mission • Long-term interdisciplinary research DFM

  4. The FLCC Team • What we bring to the Table: Expertise in • Process • New technologies • First principles modeling • Fundamental chemistry, physics • Metrology & control • Systems & Integration • What we will need to add: design, computation • Our “product” – great students, trained in the skills needed by our Industrial Partners DFM

  5. The Target – 32 and 22 nm nodes • Expected technologies • Quadrupole/Quasar illumination • Immersion Litho • Phase-shift masks • Double patterning or Double exposure • Tools that will be necessary • DfM • Distributed Computation: parallel or cellular processors • Data mining • Statistical Learning methods • Modeling, Parameter estimation DFM

  6. Three Problems • You don’t always get what you want • Interactions and The Radius of Influence • Time isn’t on your side DFM

  7. What you ask for … DFM

  8. What you get … DFM

  9. You don’t always get what you want • Manufacturing cannot be modeled by rules • Partial solution – Design tools need a better predictive “model” of the manufacturing process • Model must be simple enough to run very, very fast • Link Manufacturing model upstream to EDA tools • Static timing, RC extraction, power/noise/area optimization • Example Research Problem:  Manufacturing model to predict to 1st order transistor geometry (only 1st order because full-chip simulation is too costly)  Software to extract BSIM compatible transistor model parameters based on predicted geometry  Parameters are used in SPICE [Ref: Poppe+Capodieci – non-rectangular transistor model] DFM

  10. The Radius of Influence 90 nm 32 nm OPC/RET changes at center of red zone affects AD patterns across red area DFM

  11. The Radius of Influence • OPC/RET will get even more computationally expensive • Intelligent use of these tools • Design rules will become extremely complex • Interactions across features • Interactions between layers • Interactions among processes • Partial solution – Filter through design & process • Concentrate on design-critical hotspots • Concentrate on process sensitive hotspots DFM

  12. Iteration Cost Effectiveness of Iteration Cost of Iteration RTL $ Implementation $$ Signoff $$$ MFG Expected Volume Shipment $$$$ $$$$$$$ Schedule Delay $$$$$$$$$$$$$ Courtesy N. Jakadtar Actual Volume Shipment DFM

  13. Time isn’t on your side • Design cycle iterations are expensive • Process models must be run very fast • Design rules are now 10,000+ pages! • Computation is becoming a bottleneck • Partial solution – stress scalability and computation in all aspects of our research DFM

  14. Our Response • Five Inter-connected Research Themes • DfM • Modeling • Technology • MfD • Fundamental Studies DFM

  15. Research Theme A – DfM • Developing new algorithms, paradigms, optimization methods to better incorporate manufacturing realities into design tools for 32 nm and 22 nm nodes • Key tools – OPC software, Pattern Matching • Sample projects • Model based dummy fill and assist feature optimization (lots of work done here already!) • Incorporate LWR in models for RET and upstream in EDA tools • Systematically incorporate impact of process variation on design • New approaches to DRC handling using combination of geometric design rules and pattern matching • Statistical circuit sizing • DfM implications for mixed-signal (wide open area) DFM

  16. Research Theme B – Modeling • Develop a range of models of critical processes suitable across the design cycle for 32 nm and 22 nm nodes • Modeling • Must support a range of speeds & accuracies • Data driven • Process specific • Must assess model accuracy also • Empirical, parametric model • Model form comes from first principles • Target Processes/Effects • CMP, Etch, Mask, Flare effects in Litho, mask writing, 3D mask effects • Models for mask-less, APSM, immersion litho DFM

  17. Models for a range of Accuracy & Speeds • With the Design • Increased model resolution for optimizing critical and sensitive paths • In Design Tools • Speed-accuracy trade-off to match abstraction level RTL Synthesis Speed Prototyping Accuracy Physical Synthesis Routing Nets/Paths Optimization Sign-off Regions Courtesy N. Jakadtar DFM

  18. Modeling – Sample Research Projects • Etch Modeling for RET • OPC tools use a variable-threshold convolution kernel model • Can we build similar models for Etch? • Data driven Adaptive OPC Calibration • When minor process changes occur, use historical data to accelerate model calibration for OPC • Robust RET • RET optimization: minimize J(θ) • Objective function: J(θ) incorporates inaccurate process model • True objective J°(θ): || J(θ) – J°(θ) || < ε • How do modeling errors affect RET optimization? DFM

  19. Modeling – Sample Research Projects • Effect of process window on static timing analysis in SPICE simulations, RC extraction, and across Design tools in general • Linking TCAD to SPICE [Synopsys] • Extract process dependent parameters in standard BSIM models • Use combo of data and TCAD tools • Model maintenance • Secure model sharing DFM

  20. Research Theme C – Technology • Exploring new technologies and understanding existing technologies that could enable 32 nm and 22 nm nodes • Lithography • Double patterning, off-axis illumination, PSM, quadrupole illumination • Novel Transistor Fabrication Methods • Bulk Si transistor design to reduce performance sensitivity due to process induced variations (strain, dopant fluctuations, deep sub-λ litho) • Spacer litho defined gate electrodes for reduced sensitivity to LWR • New metrology opportunities • Damage and contamination inspection for masks/wafers DFM

  21. Research Theme D – MfD • Optimizing Manufacturing based on Info from Design Tools for 32 nm and 22 nm nodes • Hotspot detection/filtering/classification • Metrology for Manufacturability • Where to measure Optimal metrology strategies (like AMDs Adaptive Dynamic Sampling) driven thru design filters and process sensitivity filters • When to measure Driven by drift models, process models, tool models using Kalman Filtering or other systems methods DFM

  22. Research Theme E – Fundamental Studies • Understanding the fundamental aspects of critical processes that will enable 32 nm and 22 nm nodes • Theoretical understanding drives empirical, scalable model building for use in RET, and linking TCAD to EDA tools • Plasma Etch, CMP, Diffusion • Plasma surface interactions • Nano-scale etch profile shape evolution • MD tools to understand self-passivation through CF formation in etch • Chemistry and mechanics interactions at feature scale, parameterized by feature density for CMP DFM

  23. Conclusions • This is an opportunity that must be seized • Learning opportunity for our Students • Challenging multi-disciplinary research opportunity for our Faculty • Collaborative long-term leveraged research opportunity for our Industrial Partners • Outstanding problems • Need to structure and organize our research efforts cohesively • So we work as a team, not as individuals • Need close interactions with and guidance from Industry to stay on focus • Need a title!! • Need your Feedback!! DFM

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