Generative-Adversarial-Network-Guided Well-Aware Placement for Analog Circuits

Abstract

Generating wells for transistors is an essential challenge in analog circuit layout synthesis. While it is closely related to analog placement, very little research has explicitly considered well generation within the placement process. In this work, we propose a new analytical well-aware analog placer. It uses a generative adversarial network (GAN) for generating wells and guides the placement process. A global placement algorithm spreads the modules given the GAN guidance and optimizes for area and wirelength. Well-aware legalization techniques then legalize the global placement results and produce the final placement solutions. By allowing well sharing between transistors and explicitly considering wells in placement, the proposed framework achieves more than 74% improvement in the area and more than 26% reduction in half-perimeter wirelength over existing placement methodologies in experimental results.