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semiconductor-manufacturing

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Wafers-Defect-Recognition-using-Visual-Transformer

We use MixedWM38, the mixed-type wafer defect pattern dataset for wafer defect pattern regcognition with visual transformers.

  • Updated Oct 1, 2023
  • Jupyter Notebook

WaferDetect is an AI-powered wafer-map defect intelligence platform for semiconductor fabs that segments failure patterns in silicon wafers with AI, diagnoses their root causes and possible errors in the manufacturing process, and quantifies the wafer map die yield loss in dollars.

  • Updated Jul 15, 2026
  • Python

Most associations in this public semiconductor dataset are artifacts of time. 32 features clear BH-FDR with permutation p=0.0005, but only 2 survive a change of time period. Chronological split, DuckDB/SQL warehouse, SPC, cost-aware baseline, every transform fitted on train only.

  • Updated Aug 23, 2026
  • HTML

High-fidelity process simulation dashboard built in Python. Features include Deal-Grove thermal oxidation (calibrated to BYU standards), Gaussian ion implantation profiling, and selective etch rate modeling with real-time cross-sectional visualization.

  • Updated Mar 31, 2026
  • Python

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