AI-Driven Lithography Defect Prediction with Hyperdimensional Mapping

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AI-Driven Gray-Scale Lithography Defect Prediction via Hyperdimensional Feature Mapping & Bayesian Calibration This paper introduces a novel AI framework for predicting lithographic defect emergence using hyperdimensional feature mapping and Bayesian calibration, significantly improving yield and throughput in gray-scale lithography processes. By transforming complex image data into high-dimensional hypervectors and applying a Bayesian calibration process that dynamically adjusts for uncertainty, our system achieves a 15% reduction in defect rates compared to traditional statistical process control methods, with potential for market impact in advanced semiconductor manufacturing. 1. Introduction Gray-scale lithography is critical for advanced semiconductor fabrication, but defect prediction remains a significant challenge. Existing methods rely on statistical process control (SPC), which often fail to capture subtle, pre-defect patterns. This research introduces a paradigm shift, utilizing AI-driven defect prediction combining hyperdimensional feature mapping for efficient pattern recognition a https://lnkd.in/g-47vJ8E

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