Design Anthony M. Hill Fellow and Director of Processor Technology - PowerPoint PPT Presentation

Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design Anthony M. Hill Fellow and Director of Processor Technology Texas Instruments Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design

Introduction Keynotes Background Outcome Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 2

Outline Market Drivers Technology Evolution Design Method Evolution Physical Design Directions Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 3

Outline Market Drivers Technology Evolution Design Method Evolution Physical Design Directions Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 4

Automotive Markets Few sensors More sensors Fusion SV SV SV SoC Safety Alerts Transit Status & Warnings Location Data Hi-Def Maps Collaborative Mapping Passive Assist to Limited Driver Substitution Autonomous Driving with Connected Technology • • Connected compute needs active security Isolated compute provides security • • Multi-Modal Sensor Fusion provides Robustness and Redundancy Few sensors per SoC with some limited fusion • • Heavy use of Deep Learning Simple classification moving to Deep Learning ADAS Autonomous Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 5

Industrial Applications Multi Layer Architecture Cyber-physical system (CPS) based automation Today Future Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 6

IOT Overview Wearables • Entertainment • Fitness Automation • Access Control • Light and Temp Smart Cities • Residential E-meters • Smart Street lights Manufacturing • Flow Optimization • Real-time inventory Health Care • Remove Monitoring • Asset tracking Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 7

Safety for Automotive, Industrial, and IOT Lane Assist Auto Cruise • Integration has driven more potential system faults inside the SOC. Integrated SoC • We must address these with redundant or fail-safe solutions. • Physical Design plays a critical roll. ECC Diagnostic Blind Spot Monitor Collision Avoidance Watchdog Watchdog Monitor Monitor Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 8

Summary: Market Challenges • Reliability – Producing long-lived products with low failure rates. • System Integration – Board-level issues now present in SOC-level design. • Adaptability – Markets moving faster than SOC design cycle times. • Ubiquity – More sockets; more applications; lower power and distributed applications. Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 9

Outline Market Drivers Technology Evolution Design Method Evolution Physical Design Directions Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 10

(Obligatory Technology Scaling Slide) Classic Dennard Scaling The Rise of Variation Lithographic Complexity Saxena, et al. IEEE Trans. On Electron Devices, Vol 44, No1, p 131. Dennard, et al. Journal Solid State Circuits, Oct. 1974. S. Borkar, “Design challenges for gigascale integration,” presented at the 37th IEEE/ACM Int. Symp. Microarchitecture, Portland, OR, 2004. • Faster • Not cheaper • More Complex • Cheaper • Possibly not faster • Lower Power • Lower power per operation • (Much) More Complex Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 11

Cost Trends • Large-scale integration – Multi-core, multi-architecture devices. • True ‘system on a chip’ designs – Analog and complex IP integration. • Increasing development cost. – First- pass silicon ‘success’ – Emergence of ecosystem solutions Source: IBS, http://semiengineering.com/how-much-will-that-chip-cost/ Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 12

Summary: Technology Challenges • Complexity – Technology-driven complexity and system complexity. • Variability – Uncertainty in design and complexity in design signoff. • Cost – Design cost optimization to build viable products. • Ecosystem – Analog and dissimilar IP integration Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 13

Outline Market Drivers Technology Evolution Design Method Evolution Physical Design Directions Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 14

Design Evolution ~1995 ~2006 ~2018 Custom  Foundry Process Custom Process Foundry Process Custom  Ecosystem IP Custom IP Ecosystem IP Internal EDA Ecosystem EDA Ecosystem/Foundry EDA 100k  1M objects 10  100M+ objects 100M’s  B+ objects Hand + Auto Synthesis + P&R Mostly Auto Synthesis + P&R (Physical) Synthesis + P&R Cap-based Simple STA Manual ECO/Timing Closure Auto ECO/Timing Closure Simple “MHz” GPIO SI-based STA Variation-aware STA DDR, ~5GHz SERDES Analog, PM, DDR, SERDES, … Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 15

Cycle Time Compression IP Development SOC Specification SOC Assembly PD Cycles & Bug Fixes “Final Dash” Tapeout SOC Verification IP Development SOC Specification SOC Assembly PD Cycles & Bug Fixes “Final Dash” SOC Verification Tapeout • Physical Design overlap with IP development, SOC assembly, and verification. • New challenge introduced with dirty data and more iterative design. Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 16

SOC Evolution • Highly-Integrated Systems (especially IOT) • Complex Clocking & Systems – Generally driven by external interfaces, low-power communication standards, etc. – Example: 200k instance IOT design, 200 source clocks, average 12 clocks / register, 1200 total clock domains • Re-use – Investment costs drive need to re-use macros across multiple devices in a node. • Non-Traditional Advanced Node Adoption – Driven by lower power, ease-of-use, flash integration, etc. Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 17

Summary: Design Methods • Cycle Time – Overlapping PD with other domains. • Constraints – Complex constraints and dissimilar IP interactions. • Integration – Analog IP integration with unique requirements. • Advanced Nodes – Wider adoption creating QOR, TAT, and ease-of-use challenges. Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 18

Outline Market Drivers Technology Evolution Design Method Evolution Physical Design Directions Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 19

Reliability Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design

Key Metrics for Quality and Reliability DPPM FIT Defective Parts Per Million Failure in time • Quality metric • Failure Rate as a f(time) • No units of time • 1 FIT = 1 Failure / 10 9 hours • Defines quality needed Failure Rate • Focus on intrinsic reliability • Focus on extrinsic defects Early Failure Region (EFR) Safe Area Wear-out Time (not to scale) Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 21

Intrinsic Reliability Market Requirements Consumer Infrastructure Industrial Infotainment Safety Life 3-5 Years 10 Years 10 - 20 Years 10-15 Years 10-20 Years Tj 90C 105C 125C+ 125C 125C+ POH <100K 100K 100-200K 12.5 to 20K 12.5-100+K FIT 50 <50 1-5 1-5 0.1-2 ECC Minimal Critical RAMs ~ All RAMs Critical RAMs All RAMs End of Life (Wear-out) Reliability Tighter FIT Temperature extremes requirements constrain challenge reliability designs. closure. Breakdown Slow down • • Electromigration Hot-carriers • • TDDB (GOI) NBTI / PBTI Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 22

Measuring Reliability Statistical EM Current Density Consumer and Infrastructure • Simple, conservative signoff methodologies • Individual Conservative signoff to specs. component • Hard to quantify margin or wire Signoff m 3 σ High FIT Rate Industrial, Infotainment, Safety Contributor • Requires early reliability budgeting • Typically complex calculators and simulation • Conservative signoff to specs • FIT estimation models: f(process, environment, etc.) • Calculators for scaling across various use conditions Challenges and Opportunities in Automotive, Industrial, and IOT Physical Design 23