Breaking the Tuning Barrier: Zero-Hyperparameters Yield Multi-Corner Analysis Via Learned Priors

TL;DR

This paper introduces a zero-hyperparameter approach for multi-corner circuit analysis using learned priors, significantly reducing simulation costs while maintaining high accuracy without tuning.

Contribution

It replaces engineered priors with learned priors from a foundation model, enabling instant adaptation to circuits without hyperparameter tuning.

Findings

Achieves state-of-the-art accuracy with mean MRE as low as 0.11%.

Reduces validation cost by over 10 times.

Eliminates the need for hyperparameter tuning in multi-corner analysis.

Abstract

Yield Multi-Corner Analysis validates circuits across 25+ Process-Voltage-Temperature corners, resulting in a combinatorial simulation cost of $O(K \times N)$ where $K$ denotes corners and $N$ exceeds $10^4$ samples per corner. Existing methods face a fundamental trade-off: simple models achieve automation but fail on nonlinear circuits, while advanced AI models capture complex behaviors but require hours of hyperparameter tuning per design iteration, forming the Tuning Barrier. We break this barrier by replacing engineered priors (i.e., model specifications) with learned priors from a foundation model pre-trained on millions of regression tasks. This model performs in-context learning, instantly adapting to each circuit without tuning or retraining. Its attention mechanism automatically transfers knowledge across corners by identifying shared circuit physics between operating conditions. Combined with an automated feature selector (1152D to 48D), our method matches state-of-the-art accuracy (mean MREs as low as 0.11\%) with zero tuning, reducing total validation cost by over $10\times$.