SPECS: Speciated Evolutionary Circuit Synthesis

2026-07-15

AI Summary

This work addresses the challenge of jointly optimizing circuit topology and component sizing in analog circuit synthesis by adapting the NeuroEvolution of Augmenting Topologies (NEAT) algorithm for the first time in this domain. By redefining genetic representation, tailoring genetic operators, and incorporating wiring constraints alongside speciation strategies, the proposed method effectively preserves population diversity during evolution while ensuring the generation of valid circuits. The approach enables synergistic co-optimization of topology and sizing, yielding circuits that significantly outperform existing benchmarks in terms of both design quality and reliability across synthesis tasks for square, cube, square root, and cube root functions, demonstrating notable innovation and practical utility.

Abstract

We propose SPECS, a genetic algorithm for automated analog circuit synthesis with joint topology and sizing optimization. SPECS is inspired by NeuroEvolution of Augmenting Topologies (NEAT), an evolutionary algorithm originally developed to synthesize neural networks. By reformulating the genome representation and adapting the genetic operators to the analog circuit domain, we successfully transfer the core principles of NEAT to analog circuit synthesis. Circuit-specific wiring constraints are incorporated to ensure valid and physically meaningful designs throughout the evolutionary process, and speciation is used to preserve innovation while maintaining population diversity. We evaluate the proposed method on a set of computational circuit synthesis tasks consisting of square, cube, square root, and cube root functions. Experimental results demonstrate that SPECS outperforms benchmark methods across all tasks in both solution quality and reliability. The synthesized circuits and their schematics are available in the supplementary repository.

Problem

• analog circuit synthesis
• topology optimization
• sizing optimization
• automated design
• evolutionary algorithm

Innovation

• genetic algorithm
• analog circuit synthesis
• NEAT
• speciation
• topology optimization