wafer-map

STDF to Wafer Bin Map utility written in Python

Python utilities for loading, plotting, and editing wafer defect maps known as KLA Reference Files (KLARFs)

a kibana plugin to visualize the wafer map

This project aims to process 2D images of semiconductor silicon wafers to identify any defects on the wafers as well as their corresponding locations.

산학협력프로젝트: 머신러닝 기반 Wafer Map Defect Pattern Identification

Wafer map defect pattern classification with Multi-Input Neural Network using Convolutioal and Handcrafted Features

Interactive wafer map visualization and yield analysis for semiconductor test data. Rendering, spatial statistics, failure clustering, lot-level trends, and reticle analysis — pure ES modules, no dependencies.

Mask-aware wafer defect classification using a DenseNet-based CNN with explicit geometry masking and Grad-CAM explainability.

Desktop and browser wafer map viewer for semiconductor test data — Tauri v2, Rust/WASM parsers for STDF/ATDF/CSV/JSON/Parquet, wafermap rendering

Utility script for visualization of wafer properties from measurement data, intended for wafer evaluation in process development and production workflows.

Utility script for generating wafer-level measurement point layouts with configurable patterns and edge exclusion, designed for semiconductor process development and production workflows.

Utility script for visualization of wafer properties from measurement data, intended for wafer evaluation in process development and production workflows.

Wafer Map Defect Classification using Deep Convolutional Neural Networks (CNN) with TensorFlow/Keras on the WM-811K dataset.

Automatic scratch detection in semiconductor wafer maps using engineered spatial features and XGBoost.

EFA-aware wafer map failure pattern classification on WM-811K — calibrated confidence, selective prediction, honest evaluation

ResNet-18 wafer defect classifier trained on WM-811K — focal loss, CBAM attention, TTA, and Grad-CAM++ explainability. Macro-F1 0.916 on the 9-class labeled subset.

Interpretable wafer-map classification with spatial signatures, calibrated confidence and similar-case retrieval.

Semiconductor DMI yield intelligence platform for class-imbalanced defect detection, wafer-map simulation, and chamber traceback

Multimodal agentic pipeline for industrial defect triage. A vision model classifies wafer map or surface defects, retrieves similar historical cases, an LLM reasons about root cause, and a reporter agent drafts a disposition, with confidence-gated hand-off to a human reviewer.

Cost-sensitive wafer-map defect triage under label scarcity: calibrated probabilities, closed-form Bayes thresholds, and MIP review allocation on 811K real fab wafer maps (WM-811K)