HeaRT: A Hierarchical Circuit Reasoning Tree-Based Agentic Framework for AMS Design Optimization

• URL: http://arxiv.org/abs/2511.19669v1
• Date: Mon, 24 Nov 2025 20:11:06 GMT
• Title: HeaRT: A Hierarchical Circuit Reasoning Tree-Based Agentic Framework for AMS Design Optimization
• Authors: Souradip Poddar, Chia-Tung Ho, Ziming Wei, Weidong Cao, Haoxing Ren, David Z. Pan,
• Abstract summary: HeaRT is a foundational reasoning engine for automation loops and a first step toward intelligent, adaptive, human-style design optimization.<n>HeaRT consistently demonstrates reasoning accuracy 97% and Pass@1 performance 98% across our 40-circuit benchmark repository.<n>Our experiments show that HeaRT yields 3x faster convergence in both sizing and topology design adaptation tasks.
• Score: 13.18012004667103
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Conventional AI-driven AMS design automation algorithms remain constrained by their reliance on high-quality datasets to capture underlying circuit behavior, coupled with poor transferability across architectures, and a lack of adaptive mechanisms. This work proposes HeaRT, a foundational reasoning engine for automation loops and a first step toward intelligent, adaptive, human-style design optimization. HeaRT consistently demonstrates reasoning accuracy >97% and Pass@1 performance >98% across our 40-circuit benchmark repository, even as circuit complexity increases, while operating at <0.5x real-time token budget of SOTA baselines. Our experiments show that HeaRT yields >3x faster convergence in both sizing and topology design adaptation tasks across diverse optimization approaches, while preserving prior design intent.

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