GLOVA: Global and Local Variation-Aware Analog Circuit Design with Risk-Sensitive Reinforcement Learning

TL;DR

GLOVA is a novel analog circuit sizing framework that uses risk-sensitive reinforcement learning and ensemble critics to efficiently manage PVT variations, significantly reducing design time and improving robustness.

Contribution

The paper introduces GLOVA, a new variation-aware analog design method employing risk-sensitive reinforcement learning with ensemble critics for enhanced robustness and efficiency.

Findings

Achieves up to 80.5× improvement in sample efficiency.

Reduces design time by up to 76×.

Supports comprehensive PVT variation evaluation methods.

Abstract

Analog/mixed-signal circuit design encounters significant challenges due to performance degradation from process, voltage, and temperature (PVT) variations. To achieve commercial-grade reliability, iterative manual design revisions and extensive statistical simulations are required. While several studies have aimed to automate variation aware analog design to reduce time-to-market, the substantial mismatches in real-world wafers have not been thoroughly addressed. In this paper, we present GLOVA, an analog circuit sizing framework that effectively manages the impact of diverse random mismatches to improve robustness against PVT variations. In the proposed approach, risk-sensitive reinforcement learning is leveraged to account for the reliability bound affected by PVT variations, and ensemble-based critic is introduced to achieve sample-efficient learning. For design verification, we also propose $\mu$-$\sigma$ evaluation and simulation reordering method to reduce simulation costs of identifying failed designs. GLOVA supports verification through industrial-level PVT variation evaluation methods, including corner simulation as well as global and local Monte Carlo (MC) simulations. Compared to previous state-of-the-art variation-aware analog sizing frameworks, GLOVA achieves up to 80.5$\times$ improvement in sample efficiency and 76.0$\times$ reduction in time.