AINeutralarXiv – CS AI · 6h ago6/10
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Bridging the Sim-to-Real Gap in Semiconductor Visual Program Synthesis via Input Binarization
Researchers propose a visual program synthesis framework using Vision-Language Models to convert semiconductor inspection images into editable code, addressing the costly challenge of obtaining real training data for circuit metrology. By applying input binarization to strip texture noise from real Scanning Electron Microscope images, the approach bridges the gap between synthetic training data and real-world application, improving geometric accuracy detection by 19.6%.