White-Box Op-Amp Design via Human-Mimicking Reasoning

2026-01-29

AI Summary

This work addresses the lack of interpretability and reliability in black-box approaches to operational amplifier parameter design, which often fail—particularly under complex circuit topologies. To overcome this limitation, the authors propose White-Op, a novel framework that formalizes the human “hypothesis–verification–decision” reasoning process for automated circuit design. White-Op leverages a large language model agent to generate design hypotheses, formulates them as symbolic pole-zero optimization problems, and iteratively validates them through closed-form mathematical solutions, behavioral-to-transistor-level mapping, and SPICE simulations. Evaluated across nine op-amp topologies, the framework successfully produces functional designs with a theoretical prediction error of only 8.52%, substantially outperforming black-box baselines—which fail on five of the nine topologies—and thereby establishing a highly successful, interpretable, white-box automation pipeline for analog circuit design.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economic aspects and information design of Web, GenAI, and cloud computing
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

This brief proposes \emph{White-Op}, an interpretable operational amplifier (op-amp) parameter design framework based on the human-mimicking reasoning of large-language-model agents. We formalize the implicit human reasoning mechanism into explicit steps of \emph{\textbf{introducing hypothetical constraints}}, and develop an iterative, human-like \emph{\textbf{hypothesis-verification-decision}} workflow. Specifically, the agent is guided to introduce hypothetical constraints to derive and properly regulate positions of symbolically tractable poles and zeros, thus formulating a closed-form mathematical optimization problem, which is then solved programmatically and verified via simulation. Theory-simulation result analysis guides the decision-making for refinement. Experiments on 9 op-amp topologies show that, unlike the uninterpretable black-box baseline which finally fails in 5 topologies, White-Op achieves reliable, interpretable behavioral-level designs with only 8.52\% theoretical prediction error and the design functionality retains after transistor-level mapping for all topologies. White-Op is open-sourced at \textcolor{blue}{https://github.com/zhchenfdu/whiteop}.

Problem

• op-amp design
• interpretability
• white-box
• parameter optimization
• circuit topology

Innovation

• human-mimicking reasoning
• interpretable design
• hypothesis-verification-decision
• symbolic pole-zero regulation
• white-box op-amp