• [-] 6.1 Create defect classification models
  • Implement CNN models for defect type classification
  • Write image preprocessing and augmentation pipelines
  • Create training data management and labeling tools
  • Implement model evaluation and confusion matrix analysis
  • Requirements: 8.5, 8.8, 2.6

Here's a clean, professional Markdown document for Task 6.1: Create Defect Classification Models, organized with clear sections, structured file mapping, and enhanced readability.

🔍 Task 6.1: Defect Classification Models – File Mapping & System Overview

A production-ready computer vision system for semiconductor wafer defect classification, combining traditional machine learning, deep learning, and robust software engineering practices.

This document provides a comprehensive file-to-function mapping, component breakdown, and system architecture overview.

🧩 Core Implementation Files

⚙️ Configuration & Setup Files

📦 Containerization & Deployment

✅ Testing & Quality Assurance

📄 Documentation

🔍 Detailed Content Breakdown

1. Core Classification Engine (defect_classification.py)

🏗️ Classes (8)

• DefectClassificationModels: Unified interface for ML/DL models
• WaferMapPreprocessor: Image normalization, noise removal, alignment
• FeatureExtractor: Statistical, texture, spatial, and frequency domain features
• PatternDetector: Identifies defect spatial patterns
• EnsembleClassifier: Combines predictions with voting/weighting
• YieldImpactAnalyzer: Estimates yield loss from defect severity and location
• ModelTrainer: Handles training pipeline
• DefectVisualizer: Generates annotated output maps

🤖 Models

• Traditional ML: Random Forest, SVM, Gradient Boosting, k-NN
• Deep Learning: CNN, ResNet, EfficientNet (transfer learning)

📐 Features (50+)

🎯 Defect Patterns (5)

• Center: Concentric around wafer center
• Edge: Ring near perimeter
• Scratch: Linear or curved streaks
• Donut: Circular void in center
• Random: Scattered, non-structured

🔄 Processing Pipeline

1. Image preprocessing (noise reduction, contrast enhancement)
2. Data augmentation (rotation, flip, zoom)
3. Feature extraction
4. Ensemble prediction (ML + DL fusion)
5. Yield impact estimation

2. FastAPI Service (wafer_vision_service.py)

🌐 Endpoints (8)

🛠 Key Features

• Pydantic models for request/response validation
• Async processing for large batches
• File upload support (PNG, TIFF, JPEG)
• Logging & metrics integration
• Error handling with structured responses

3. Configuration (defect_config.yaml)

4. Docker Setup (Dockerfile + docker-compose.yml)

🐳 Dockerfile (Multi-Stage)

🧩 Docker Compose Services (8)

1. wafer-vision-api (CPU)
2. wafer-vision-gpu (GPU-accelerated)
3. redis
4. postgresql
5. prometheus
6. grafana
7. alertmanager
8. dev-env (Jupyter + testing)

Includes health checks, resource limits, volume mounts, and internal networking.

5. Testing (test_defect_classification.py)

🧪 Test Classes (6)

• TestPreprocessor: Image normalization and alignment
• TestFeatureExtractor: Feature correctness and stability
• TestMLModels: Prediction accuracy and training
• TestDLModels: CNN/ResNet inference and fine-tuning
• TestPatternDetector: Spatial pattern recognition
• TestIntegration: End-to-end classification workflow

✅ Coverage

• Unit tests for all core functions
• Integration tests (API → model → output)
• Edge cases: corrupted images, missing data, invalid formats
• Performance benchmarks (latency, throughput)

🧩 Fixtures

• Synthetic wafer map generator
• Mock defect images
• Simulated sensor noise

6. Documentation (README.md)

🔄 Key Relationships & Dependencies

graph TD
    A[defect_classification.py] -->|Used by| B[wafer_vision_service.py]
    B -->|Configured via| C[defect_config.yaml]
    C -->|Containerized in| D[Dockerfile + docker-compose.yml]
    D -->|Tested by| E[test_defect_classification.py]
    E -->|Documented in| F[README.md]

This dependency flow ensures modularity, testability, and reproducibility across environments.

✅ System Capabilities & Value

This implementation delivers a production-grade wafer defect classification system with:

• High Accuracy: Ensemble ML/DL models for reliable defect identification
• Scalability: Containerized, supports batch and real-time processing
• Flexibility: Configurable for different defect types, tools, and fabs
• Maintainability: Well-tested, documented, and modular
• Deployability: Supports CPU and GPU environments

🚀 Conclusion

The Defect Classification System is now complete and ready for integration into the semiconductor manufacturing workflow.

✅ Accurate – Combines 50+ features and multiple models

📦 Deployable – Dockerized with full orchestration

🧪 Reliable – Comprehensive testing and error handling

📚 Maintainable – Fully documented and structured

This system enables automated, real-time defect detection and yield impact assessment, supporting higher quality and efficiency in wafer fabrication.

✅ Status: Production-Ready

📁 All components version-controlled, tested, and deployable via CI/CD.