Design Document: Semiconductor AI Ecosystem

Overview

The Semiconductor AI Ecosystem is a comprehensive platform that integrates open-source Large Language Models with semiconductor manufacturing data to accelerate yield learning, optimize processes, and enable predictive analytics. The system employs a microservices architecture with containerized deployment, supporting both cloud and on-premises environments while maintaining strict data security and compliance requirements.

The platform serves as an intelligent manufacturing assistant that can analyze complex semiconductor data patterns, provide root cause analysis, predict equipment failures, and recommend process optimizations based on historical data and domain expertise.

Architecture

High-Level System Architecture

Diagram

Deployment Architecture

The system supports three deployment patterns:

Hybrid Cloud: Critical data processing on-premises with AI inference in secure cloud environments
On-Premises: Complete deployment within fab infrastructure for maximum security
Multi-Cloud: Distributed deployment across multiple cloud providers for redundancy

Components and Interfaces

1. Data Ingestion Layer

ETL Pipeline Service

Technology: Apache Airflow, Apache Spark
Purpose: Batch processing of historical data from MES, WAT, CP, and Yield systems
Interfaces:
- SEMI SECS/GEM protocol adapters
- REST API connectors
- Database connectors (Oracle, SQL Server, PostgreSQL)
Data Processing: Data validation, cleansing, transformation, and enrichment
Scheduling: Configurable batch schedules with dependency management

Stream Processing Service

Technology: Apache Kafka, Apache Flink
Purpose: Real-time processing of equipment sensor data and alerts
Interfaces:
- MQTT brokers for IoT sensor data
- Equipment-specific APIs (Applied Materials, KLA, AMAT)
- Message queues for high-frequency data streams
Processing: Real-time anomaly detection, data aggregation, and event correlation

Data API Gateway

Technology: Kong, AWS API Gateway, or Azure API Management
Purpose: Unified interface for data access and ingestion
Features:
- Rate limiting and throttling
- Authentication and authorization
- API versioning and documentation
- Request/response transformation

2. Data Storage Layer

Data Warehouse

Technology: Snowflake, Amazon Redshift, or Azure Synapse
Schema: Star schema optimized for analytical queries
Data Models:
- Fact tables: Process measurements, test results, defect counts
- Dimension tables: Equipment, recipes, lots, wafers, time
Partitioning: Time-based partitioning for efficient querying
Retention: Configurable data retention policies

Data Lake

Technology: Apache Iceberg on S3/ADLS, or Delta Lake
Storage Format: Parquet with schema evolution support
Organization:
- Raw zone: Unprocessed data from source systems
- Curated zone: Cleaned and validated data
- Analytics zone: Aggregated data for ML training
Governance: Data lineage tracking and quality monitoring

Vector Database

Technology: Pinecone, Weaviate, or Chroma
Purpose: Store embeddings for RAG system
Content Types:
- Process documentation embeddings
- Historical failure analysis embeddings
- Equipment manual embeddings
- Best practice document embeddings
Indexing: HNSW or IVF indexing for fast similarity search

Caching Layer

Technology: Redis Cluster
Purpose: Cache frequently accessed data and model predictions
Cache Types:
- Query result caching
- Model prediction caching
- Session data caching
- Real-time metrics caching

3. AI/ML Layer

LLM Service

Models:
- Primary: Llama 3 70B or Qwen 72B for complex reasoning
- Secondary: Mistral 7B for faster responses
- Specialized: CodeLlama for process recipe analysis
Deployment:
- Model serving via TensorRT or vLLM
- GPU clusters (NVIDIA A100/H100)
- Auto-scaling based on request volume
Fine-tuning: LoRA adapters for semiconductor domain adaptation

RAG Engine

Architecture:
- Query understanding and intent classification
- Semantic search across vector database
- Context ranking and selection
- Response generation with source attribution
Components:
- Embedding model: sentence-transformers or OpenAI embeddings
- Retrieval: Dense passage retrieval with re-ranking
- Generation: Context-aware response generation
Optimization: Query expansion, result fusion, and relevance feedback

ML Model Service

Model Types:
- Time-series forecasting: Prophet, LSTM, Transformer models
- Anomaly detection: Isolation Forest, Autoencoders
- Computer vision: CNN models for wafer map analysis
- Predictive maintenance: Gradient boosting models
MLOps:
- Model registry (MLflow)
- Automated training pipelines
- A/B testing framework
- Model monitoring and drift detection

Inference Engine

Technology: NVIDIA Triton Inference Server or TorchServe
Features:
- Multi-model serving
- Dynamic batching
- Model versioning
- Performance optimization
Scaling: Horizontal pod autoscaling based on metrics

4. Application Layer

Conversational AI Interface

Technology: React/Vue.js frontend with WebSocket connections
Features:
- Natural language query interface
- Multi-turn conversations with context
- File upload for analysis (wafer maps, reports)
- Voice input/output capabilities
Integration: Direct connection to LLM service via API gateway

Analytics Dashboard

Technology: Grafana, Tableau, or custom React dashboard
Visualizations:
- Real-time equipment health monitoring
- Yield trend analysis and forecasting
- Process parameter correlation heatmaps
- Defect pattern visualization
Interactivity: Drill-down capabilities and custom filtering

Alert and Notification System

Technology: Apache Kafka for event streaming
Alert Types:
- Process excursion alerts
- Equipment failure predictions
- Yield anomaly notifications
- Model performance degradation alerts
Delivery: Email, SMS, Slack, Microsoft Teams integration

Data Models

Core Data Entities

Wafer Entity

{
  "wafer_id": "string",
  "lot_id": "string",
  "product_id": "string",
  "process_flow": "string",
  "start_time": "timestamp",
  "completion_time": "timestamp",
  "current_step": "string",
  "status": "enum[in_progress, completed, scrapped]",
  "yield_data": {
    "electrical_yield": "float",
    "parametric_yield": "float",
    "visual_yield": "float"
  }
}

Process Step Entity

{
  "step_id": "string",
  "wafer_id": "string",
  "equipment_id": "string",
  "recipe_id": "string",
  "chamber_id": "string",
  "start_time": "timestamp",
  "end_time": "timestamp",
  "parameters": {
    "temperature": "float",
    "pressure": "float",
    "flow_rates": "object",
    "rf_power": "float"
  },
  "measurements": {
    "thickness": "float",
    "cd": "float",
    "overlay": "float"
  }
}

Equipment Entity

{
  "equipment_id": "string",
  "equipment_type": "string",
  "manufacturer": "string",
  "model": "string",
  "chambers": ["array of chamber_ids"],
  "health_score": "float",
  "maintenance_schedule": "object",
  "sensor_data": {
    "timestamp": "timestamp",
    "sensors": "object"
  }
}

Defect Entity

{
  "defect_id": "string",
  "wafer_id": "string",
  "inspection_step": "string",
  "defect_type": "enum[particle, scratch, residue, bridging]",
  "coordinates": {
    "x": "float",
    "y": "float"
  },
  "size": "float",
  "severity": "enum[critical, major, minor]",
  "image_path": "string"
}

Knowledge Base Schema

Document Entity

{
  "document_id": "string",
  "title": "string",
  "type": "enum[sop, bkm, manual, report]",
  "content": "text",
  "embeddings": "vector",
  "metadata": {
    "process_area": "string",
    "equipment_type": "string",
    "last_updated": "timestamp",
    "version": "string"
  }
}