Presentation
Adaptive AI and Additive AI Techniques for High-Sigma Standard Cell Verification
DescriptionAs chip complexity reaches tens of billions of transistors, standard cells are duplicated millions of times, making fast and accurate high‑sigma verification essential. Fixed‑sigma approaches are no longer viable. Each cell requires a flexible sigma target to avoid redesign and to enable yield‑based repurposing rather than discarding.
We present a fully automated, AI‑driven methodology that verifies an entire standard cell library in a single pass. Adaptive AI tailors verification jobs to individual cells, while Additive AI iteratively refines models across multiple PVT and input‑vector conditions, delivering brute‑force‑level accuracy without additional simulations.
The Worst‑Case Yield Solver (WCYS) identifies near‑target and worst‑case samples in the Solido PVTMC Verifier, builds predictive models, and triggers a reinforcement‑learning‑based High‑Sigma Verifier only when sign‑off criteria demand. Dynamic Constraint Yield Sign‑off (DCYS) automatically sweeps failing‑cell constraints until they pass, reducing the number of failed cells by 2.4×.
Compared with traditional scaled Monte‑Carlo methods, the proposed flow achieves a ten‑fold speedup, a tighter confidence interval (6.000 [5.900–6.098] vs 6.180 [5.067–7.146]), and a 60 % reduction in cells failing between 0.5 V and 0.6 V. This AI‑enabled verification provides 6‑sigma‑level confidence across massive libraries while substantially reducing verification time and resource consumption.
We present a fully automated, AI‑driven methodology that verifies an entire standard cell library in a single pass. Adaptive AI tailors verification jobs to individual cells, while Additive AI iteratively refines models across multiple PVT and input‑vector conditions, delivering brute‑force‑level accuracy without additional simulations.
The Worst‑Case Yield Solver (WCYS) identifies near‑target and worst‑case samples in the Solido PVTMC Verifier, builds predictive models, and triggers a reinforcement‑learning‑based High‑Sigma Verifier only when sign‑off criteria demand. Dynamic Constraint Yield Sign‑off (DCYS) automatically sweeps failing‑cell constraints until they pass, reducing the number of failed cells by 2.4×.
Compared with traditional scaled Monte‑Carlo methods, the proposed flow achieves a ten‑fold speedup, a tighter confidence interval (6.000 [5.900–6.098] vs 6.180 [5.067–7.146]), and a 60 % reduction in cells failing between 0.5 V and 0.6 V. This AI‑enabled verification provides 6‑sigma‑level confidence across massive libraries while substantially reducing verification time and resource consumption.
Event Type
Engineering Presentation
TimeMonday, July 271:45pm - 2:00pm PDT
LocationSeaside Ballroom B
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