Accelerating OTA Circuit Design: Transistor Sizing Based on a Transformer Model and Precomputed Lookup Tables

TL;DR

This paper introduces a transformer-based framework for automated transistor sizing in OTAs, significantly reducing simulation time and improving efficiency by predicting parameters with minimal SPICE simulations.

Contribution

It presents a novel transformer model combined with lookup tables for fast, accurate OTA transistor sizing, outperforming traditional and machine learning methods.

Findings

Achieves over 90% success with one SPICE simulation

Attains 100% success with 3-5 additional simulations

Reduces reliance on expensive SPICE simulations during optimization

Abstract

Device sizing is crucial for meeting performance specifications in operational transconductance amplifiers (OTAs), and this work proposes an automated sizing framework based on a transformer model. The approach first leverages the driving-point signal flow graph (DP-SFG) to map an OTA circuit and its specifications into transformer-friendly sequential data. A specialized tokenization approach is applied to the sequential data to expedite the training of the transformer on a diverse range of OTA topologies, under multiple specifications. Under specific performance constraints, the trained transformer model is used to accurately predict DP-SFG parameters in the inference phase. The predicted DP-SFG parameters are then translated to transistor sizes using a precomputed look-up table-based approach inspired by the gm/Id methodology. In contrast to previous conventional or machine-learning-based methods, the proposed framework achieves significant improvements in both speed and computational efficiency by reducing the need for expensive SPICE simulations within the optimization loop; instead, almost all SPICE simulations are confined to the one-time training phase. The method is validated on a variety of unseen specifications, and the sizing solution demonstrates over 90% success in meeting specifications with just one SPICE simulation for validation, and 100% success with 3-5 additional SPICE simulations.