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

2026-01-29Code Available0· sign in to hype

Zihao Chen, Jiayin Wang, Ziyi Sun, Ji Zhuang, Jinyi Shen, Xiaoyue Ke, Li Shang, Xuan Zeng, Fan Yang

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Abstract

This brief proposes 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 introducing hypothetical constraints, and develop an iterative, human-like 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 bluehttps://github.com/zhchenfdu/whiteop.

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