Task:Integrate semiconductor domain knowledge

[ ] 4.3 Integrate semiconductor domain knowledge

Implement SEMI and JEDEC standards knowledge base
Create process terminology and equipment specification embeddings
Write domain-specific query expansion and processing
Implement semiconductor-specifi…


This content originally appeared on DEV Community and was authored by Y.C Lee

  • [ ] 4.3 Integrate semiconductor domain knowledge
    • Implement SEMI and JEDEC standards knowledge base
    • Create process terminology and equipment specification embeddings
    • Write domain-specific query expansion and processing
    • Implement semiconductor-specific response validation
    • Requirements: 1.2, 1.4, 3.1, 3.2

Here is a well-organized summary of Task 4.3 Semiconductor Domain Knowledge Integration, including components, features, integration points, and capabilities:

✅ Task 4.3 Complete: Semiconductor Domain Knowledge Integration

Core Components Created

  • Comprehensive Knowledge Base (semiconductor_knowledge.py):

    • Covers key semiconductor processes such as lithography, etch, deposition, CMP, implant, anneal.
    • Equipment database with details for major vendors: Applied Materials, ASML, KLA, LAM Research, etc.
    • Integration of industry standards including SEMI and JEDEC specifications.
    • Defect classification framework and material properties database.
    • Catalog of measurement techniques relevant to semiconductor manufacturing.

  • REST API Service (knowledge_service.py):

    • FastAPI service exposing comprehensive endpoints for domain knowledge operations.
    • Supports knowledge search with fuzzy matching capabilities.
    • Parameter validation against rigorous knowledge base constraints.
    • Generates troubleshooting guidance contextualized by issue type and equipment.
    • Provides process optimization recommendations based on historical and analytical data.
    • Exports knowledge data for external use.

  • Supporting Infrastructure:

    • Containerized deployment with multi-service setup for scalability.
    • Redis caching employed for enhanced performance and response times.
    • PostgreSQL used for persistent, reliable storage of knowledge base.
    • Includes a comprehensive test suite for validating functionality.
    • Monitoring integrated with Prometheus and metrics gathering for operational insights.
    • Configuration management to support multiple environments.

Key Features Implemented

  • Intelligent Search: Context-aware search spanning all knowledge categories for precise and relevant results.
  • Parameter Validation: Verifies process parameters with tolerance checks aligned to industry standards.
  • Troubleshooting System: Delivers targeted recommendations for issue resolution based on process context and equipment.
  • Process Optimization: Generates data-driven suggestions to optimize manufacturing processes and performance.
  • Industry Standards Compliance: Deep integration of SEMI, JEDEC, and operational guidelines.
  • Equipment Expertise: Detailed records on equipment specifications, maintenance needs, and troubleshooting instructions.

Integration Points

  • RAG Engine: Supplies domain-specific context enriching AI-generated responses.
  • Vector Database: Enables semantic search across technical knowledge assets.
  • Analytics Dashboard: Incorporates domain expertise for enhanced data interpretation and visualization.
  • Chat Interface: Provides domain-aware interactions enhancing user experience with semiconductor expertise.

Here is a comprehensive and structured mapping of Task 4.3 item-to-file relationships with content descriptions for the Semiconductor Domain Knowledge Integration system:

📋 Task 4.3: Semiconductor Domain Knowledge Integration - File Mapping & Content

Core Knowledge Base System

Component File Path Content Description
Main Knowledge Base services/ai-ml/knowledge-integration/src/semiconductor_knowledge.py Comprehensive knowledge base covering semiconductor manufacturing including process steps, equipment specs, industry standards (SEMI, JEDEC), defect classifications, material properties, and measurement techniques.
REST API Service services/ai-ml/knowledge-integration/src/knowledge_service.py FastAPI service exposing endpoints for knowledge search, parameter validation, troubleshooting guidance, and process optimization recommendations.
Logging Utilities services/ai-ml/knowledge-integration/utils/logging_utils.py Standardized logging configuration used by knowledge service components.

Configuration & Deployment

Component File Path Content Description
Service Configuration services/ai-ml/knowledge-integration/config/knowledge_config.yaml YAML configuration capturing knowledge base settings, API configuration, caching strategies, monitoring setups, and system integrations.
Docker Compose services/ai-ml/knowledge-integration/docker-compose.yml Multi-service deployment stack including knowledge service, Redis caching, PostgreSQL database, and monitoring infrastructure.
Dockerfile services/ai-ml/knowledge-integration/Dockerfile Container specification for the knowledge integration service with Python 3.11 and all required dependencies.
Dependencies services/ai-ml/knowledge-integration/requirements.txt Python package dependencies including FastAPI, Redis, PostgreSQL, machine learning libraries, and testing frameworks.

Testing & Quality Assurance

Component File Path Content Description
Unit Tests services/ai-ml/knowledge-integration/tests/test_semiconductor_knowledge.py Extensive unit tests verifying knowledge base functionality, search accuracy, parameter validation, and data integrity.
Integration Tests services/ai-ml/knowledge-integration/tests/test_integration.py API endpoint tests covering performance, correctness, and error handling scenarios.

Documentation

Component File Path Content Description
Service Documentation services/ai-ml/knowledge-integration/README.md Comprehensive documentation covering system features, API references, configuration options, development notes, and troubleshooting guidance.

Key Content Highlights

  1. Semiconductor Knowledge Base (semiconductor_knowledge.py):

    • Process knowledge modules including lithography, etch, deposition, CMP, implant, and anneal.
    • Detailed equipment database covering major vendors (Applied Materials, ASML, KLA, etc.) with specs and troubleshooting guides.
    • Industry standards compliance referencing SEMI E10/E30/E40/E90/E94 and JEDEC specifications.
    • Defect classification system covering particles, scratches, residues with causality and prevention methods.
    • Material properties database and measurement techniques catalog (e.g., critical dimension (CD) measurement, overlay, thickness).

  2. REST API Service (knowledge_service.py):

    • Search endpoints supporting fuzzy matching across knowledge categories.
    • Parameter validation endpoint enforcing tolerances from domain standards.
    • Troubleshooting and optimization recommendation endpoints providing actionable guidance.

  3. Configuration System (knowledge_config.yaml):

    • Flexible data source integration supporting file and database updates.
    • Search tuning parameters and caching strategy using Redis.
    • API settings including CORS, rate limiting, authentication, and monitoring hooks.

  4. Testing Framework:

    • Unit tests emphasizing data integrity and search correctness.
    • Integration tests stress-testing concurrent API requests, performance metrics, and robust error handling.

  5. Deployment Infrastructure:

    • Multi-service Docker Compose stack orchestrating knowledge service, Redis, PostgreSQL, and monitoring tools.
    • Monitoring using Prometheus metrics and Grafana dashboards.
    • Scalability via load balancing and distributed caching support enhancing availability.


This content originally appeared on DEV Community and was authored by Y.C Lee


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