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AI model bridges sim-to-real gap in semiconductor visual program synthesis

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel visual program synthesis framework to address the sim-to-real gap in semiconductor inspection. This approach uses a Vision-Language Model (VLM) to translate inspection images into editable code, allowing for precise geometric control and data generation. By employing an input binarization strategy, the model can effectively strip away noise and texture from real-world SEM images, enabling it to focus on crucial geometric structures and improving accuracy. AI

IMPACT This research could lead to more accurate and efficient semiconductor inspection systems by improving the training of AI models on real-world data.

RANK_REASON The cluster contains an academic paper detailing a new research methodology.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yusuke Ohtsubo, Kota Dohi, Koichiro Yawata, Koki Takeshita, Tatsuya Sasaki ·

    Bridging the Sim-to-Real Gap in Semiconductor Visual Program Synthesis via Input Binarization

    arXiv:2606.02434v1 Announce Type: new Abstract: Precise parametric control over circuit geometry is essential for semiconductor inspection, yet obtaining sufficient real training data remains costly. Although generative models such as diffusion models and Generative Adversarial N…

  2. arXiv cs.AI TIER_1 English(EN) · Tatsuya Sasaki ·

    Bridging the Sim-to-Real Gap in Semiconductor Visual Program Synthesis via Input Binarization

    Precise parametric control over circuit geometry is essential for semiconductor inspection, yet obtaining sufficient real training data remains costly. Although generative models such as diffusion models and Generative Adversarial Networks (GANs) can augment training data, they c…

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