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[P] Prompt optimization for analog circuit placement — 97% of expert quality, zero training data

Reddit r/MachineLearning / 3/24/2026

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Key Points

  • The article presents a prompt-optimization approach for analog IC circuit placement, highlighting the benchmark’s difficulty due to spatial reasoning and multi-objective constraints like matching, parasitics, and routing.
  • VizPy’s method improves an LLM’s layout reasoning through an iterative optimization loop built from failure→success examples, without requiring domain-specific training data.
  • The reported outcome is that the optimized prompts achieve performance at about 97% of expert quality on the analog circuit placement task.
  • It emphasizes that the approach is specifically designed around using LLM prompting rather than training new models with specialized analog design datasets.
  • The post invites discussion of the benchmark setup and optimization loop details in the comments.

Analog IC layout is a notoriously hard AI benchmark: spatial reasoning, multi-objective optimization (matching, parasitics, routing), and no automated P&R tools like digital design has.

We evaluated VizPy's prompt optimization on this task. The optimizer learns from failure→success pairs and improves the LLM's layout reasoning across iterations — no domain-specific training data required.

Results and methodology: https://vizops.ai/blog/prompt-optimization-analog-circuit-placement/

Happy to discuss the benchmark setup and optimization loop in comments.

submitted by /u/se4u
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