White-Op uses LLM agents for interpretable op-amp behavioral design via formalized symbolic reasoning, pole-zero handling, and iterative simulation-based refinement, succeeding on all 9 tested topologies with 8.52% average error where black-box methods fail.
Ampagent: An llm-based multi-agent system for multi-stage amplifier schematic design from literature for process and performance porting
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 3representative citing papers
AnalogMaster applies large language models to end-to-end analog IC design automation, converting images to netlists and optimizing parameters to achieve 92.9% Pass@1 and 99.9% Pass@5 success on 15 test circuits using GPT-5.
A self-calibrating system derives analytical sizing equations via LLM for analog circuits and calibrates them from a single DC simulation to achieve fast convergence.
citing papers explorer
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LLM-Assisted Op-Amp Behavioral-Level Design via Agentic Human-Mimicking Reasoning
White-Op uses LLM agents for interpretable op-amp behavioral design via formalized symbolic reasoning, pole-zero handling, and iterative simulation-based refinement, succeeding on all 9 tested topologies with 8.52% average error where black-box methods fail.
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AnalogMaster: Large Language Model-based Automated Analog IC Design Framework from Image to Layout
AnalogMaster applies large language models to end-to-end analog IC design automation, converting images to netlists and optimizing parameters to achieve 92.9% Pass@1 and 99.9% Pass@5 success on 15 test circuits using GPT-5.
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A Self-Calibrating Framework for Analog Circuit Sizing Using LLM-Derived Analytical Equations
A self-calibrating system derives analytical sizing equations via LLM for analog circuits and calibrates them from a single DC simulation to achieve fast convergence.