VPEvolve: A Self-Evolving Virtual Process Engineer for Computational Lithography

2026-09-26

AI Summary

This study addresses the challenges of complex rule interactions and fragmented expert knowledge in lithographic optical proximity correction (OPC) by proposing a large language model (LLM)-based virtual process engineer framework. Integrating a skill library with layout analysis, the method constructs evidence chains from measurement feedback under fixed model weights, leveraging an LLM reflection mechanism to guide iterative recipe editing for self-evolving corrections. The framework is further integrated with commercial tools to enable closed-loop evaluation. Experimental results demonstrate that the proposed approach significantly reduces the maximum edge placement error (EPE) on both Poly and Metal1 layers, with all final recipes satisfying quality constraints. This work provides an effective paradigm for intelligent lithography optimization.

Abstract

Optical proximity correction (OPC) recipes grow as engineers add local rules to repair newly discovered lithography hotspots. Each correction can interact with existing rules, while lessons from commercial-tool trials remain scattered across code and logs. \system combines a Virtual Process Engineer (VPE) harness with a Skill Bank of measured engineering experience. The harness equips a frozen language model with process manuals, layout analysis, recipe editing, and commercial-tool evaluation. The actor proposes changes to the global parameters, local targeted rules, or diagnostic trials. After each evaluation, an LLM reflector and curator turn the measured response into evidence-linked judgments. The actor retrieves them before its next trial. Feasible improvements update the retained recipe; every measured trial informs the Skill Bank that guides the next edit. The model weights remain fixed. On a FreePDK45-derived benchmark with ten commercial-tool evaluations per case, \system reduces the mean per-case maximum edge placement error from 18.294 to 5.361 nm on Poly and from 22.052 to 15.692 nm on Metal1. Every final recipe satisfies the predefined quality constraints and improves the maximum error by at least 0.1 nm.

Problem

• Optical Proximity Correction
• Computational Lithography
• Lithography Hotspots
• Recipe Optimization
• Edge Placement Error

Innovation

• Optical Proximity Correction
• Self-Evolving System
• Large Language Model
• Skill Bank
• Computational Lithography