SetupKit: Efficient Multi-Corner Setup/Hold Time Characterization Using Bias-Enhanced Interpolation and Active Learning

TL;DR

SetupKit is a novel framework that significantly accelerates multi-corner setup/hold time characterization in chip design by combining bias-enhanced interpolation, active learning, and circuit analysis, reducing CPU time by 2.4x.

Contribution

It introduces BEIRA, an interpolation method, and an active learning strategy for efficient PVT corner exploration, addressing slow convergence and exploration inefficiencies.

Findings

2.4x reduction in CPU time for characterization

Effective active learning guides simulations to informative corners

Significant improvement over standard practices in industrial settings

Abstract

Accurate setup/hold time characterization is crucial for modern chip timing closure, but its reliance on potentially millions of SPICE simulations across diverse process-voltagetemperature (PVT) corners creates a major bottleneck, often lasting weeks or months. Existing methods suffer from slow search convergence and inefficient exploration, especially in the multi-corner setting. We introduce SetupKit, a novel framework designed to break this bottleneck using statistical intelligence, circuit analysis and active learning (AL). SetupKit integrates three key innovations: BEIRA, a bias-enhanced interpolation search derived from statistical error modeling to accelerate convergence by overcoming stagnation issues, initial search interval estimation by circuit analysis and AL strategy using Gaussian Process. This AL component intelligently learns PVT-timing correlations, actively guiding the expensive simulations to the most informative corners, thus minimizing redundancy in multicorner characterization. Evaluated on industrial 22nm standard cells across 16 PVT corners, SetupKit demonstrates a significant 2.4x overall CPU time reduction (from 720 to 290 days on a single core) compared to standard practices, drastically cutting characterization time. SetupKit offers a principled, learningbased approach to library characterization, addressing a critical EDA challenge and paving the way for more intelligent simulation management.