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