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Y.C Lee
Y.C Lee

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Requirements for comprehensive AI ecosystem for semiconductor wafer manufacturing data analysis

Requirements Document

Introduction

This document outlines the requirements for building a comprehensive AI ecosystem for semiconductor wafer manufacturing data analysis. The system will integrate open-source Large Language Models (LLMs) such as Llama, Mistral, or Qwen with semiconductor domain-specific knowledge including SEMI standards, JEDEC specifications, and fab-specific process knowledge. The platform will unify data from Manufacturing Execution Systems (MES), Advanced Process Control (APC), Fault Detection & Classification (FDC), Wafer Acceptance Test (WAT), Circuit Probe (CP), inline defect inspection, yield management systems, equipment health monitoring, and lot genealogy tracking.

The AI ecosystem must support semiconductor manufacturing workflows including process excursion detection, yield learning, equipment predictive maintenance, recipe optimization, and statistical process control. The solution will accelerate new product introduction (NPI) ramp-up, improve overall equipment effectiveness (OEE), reduce cycle time, and maximize die yield through intelligent data correlation and root cause analysis.

Requirements

Requirement 1: LLM Model Integration and Customization

User Story: As a senior engineering manager, I want to integrate open-source LLM models with semiconductor domain expertise, so that we can build a private model tailored for our manufacturing analysis needs.

Acceptance Criteria

  1. WHEN selecting an open-source LLM foundation model THEN the system SHALL support models with minimum 7B parameters (Llama 2/3, Mistral 7B, Qwen, or CodeLlama) capable of technical reasoning
  2. WHEN integrating semiconductor domain knowledge THEN the system SHALL incorporate SEMI E10 (SECS/GEM), SEMI E30 (GEM), SEMI E40 (CIM), SEMI E90 (Substrate Tracking), SEMI E94 (Control Job Management), and JEDEC standards
  3. WHEN fine-tuning the model THEN the system SHALL use Retrieval-Augmented Generation (RAG) with vector embeddings of process recipes, failure analysis reports, yield learning documents, and equipment specifications
  4. WHEN processing semiconductor terminology THEN the system SHALL understand process steps (lithography, etch, deposition, CMP, implant, anneal), metrology parameters (CD, overlay, thickness, resistivity), and defect classifications (particles, scratches, residue, bridging)
  5. WHEN implementing domain adaptation THEN the system SHALL support Low-Rank Adaptation (LoRA) or QLoRA fine-tuning techniques for efficient model customization
  6. IF the model requires updates THEN the system SHALL support incremental learning with new process data, equipment logs, and yield analysis results
  7. WHEN deploying the private model THEN the system SHALL implement on-premises or private cloud deployment with encryption at rest and in transit
  8. WHEN handling proprietary data THEN the system SHALL ensure no data leakage to external model providers and maintain ITAR/EAR compliance if applicable

Requirement 2: Multi-Source Data Integration

User Story: As a data analyst, I want to integrate data from MES, APC, FDC, WAT, CP, Defect, Yield, Tool health systems, so that I can perform comprehensive traceability analysis across all manufacturing stages.

Acceptance Criteria

  1. WHEN connecting to MES systems THEN the system SHALL extract lot genealogy, wafer history, process step completion status, recipe parameters, chamber assignments, and operator actions via SEMI SECS/GEM or REST APIs
  2. WHEN accessing APC data THEN the system SHALL retrieve real-time process control parameters (temperature, pressure, flow rates, RF power), control limits, SPC violations, and R2R (run-to-run) adjustments from systems like Applied Materials' ControlWorks or KLA's eSPC
  3. WHEN integrating FDC data THEN the system SHALL capture multivariate fault signatures, equipment health scores, sensor drift patterns, and predictive maintenance alerts from platforms like PDF Solutions' Exensio or proprietary FDC systems
  4. WHEN processing WAT data THEN the system SHALL include electrical test parameters (Vth, Idsat, leakage current, capacitance), process control monitor (PCM) results, and parametric yield trends from Keysight, Teradyne, or Advantest test systems
  5. WHEN incorporating CP data THEN the system SHALL integrate die-level test results, bin maps, yield maps, parametric distributions, and test program versions from probe test systems
  6. WHEN analyzing defect data THEN the system SHALL correlate inline inspection results (KLA, Applied Materials, Hitachi defect inspection tools), defect density maps, Pareto analysis, and systematic vs. random defect classification
  7. WHEN evaluating yield data THEN the system SHALL provide wafer-level yield, die yield, parametric yield, and yield learning analysis with statistical correlations across process variables
  8. WHEN monitoring tool health THEN the system SHALL track equipment utilization, mean time between failures (MTBF), preventive maintenance schedules, consumable usage, and chamber matching data from equipment suppliers' monitoring systems
  9. WHEN processing time-series data THEN the system SHALL handle high-frequency sensor data (1Hz to 1kHz sampling rates) and batch processing of historical data spanning multiple years
  10. WHEN ensuring data quality THEN the system SHALL implement data validation, outlier detection, missing data imputation, and data lineage tracking

Requirement 3: Knowledge Base Integration

User Story: As a process engineer, I want the AI system to access our company's knowledge base including SOPs, BKMs, databases, and data warehouses, so that recommendations are based on our established procedures and historical learnings.

Acceptance Criteria

  1. WHEN querying SOPs THEN the system SHALL retrieve process-specific standard operating procedures including equipment setup, recipe parameters, safety protocols, and quality checkpoints with semantic search capabilities
  2. WHEN accessing BKMs THEN the system SHALL incorporate best known methods for yield enhancement, process optimization, equipment troubleshooting, and contamination control with contextual relevance scoring
  3. WHEN connecting to operational databases THEN the system SHALL maintain real-time synchronization with Oracle, SQL Server, or PostgreSQL databases containing equipment logs, maintenance records, and process data
  4. WHEN interfacing with data marts THEN the system SHALL access pre-aggregated analytical data from dimensional models including time-series summaries, yield trends, and equipment performance KPIs
  5. WHEN querying data warehouses THEN the system SHALL perform complex historical trend analysis using star schema or snowflake schema designs with support for OLAP operations
  6. WHEN processing unstructured documents THEN the system SHALL extract knowledge from PDF reports, Word documents, PowerPoint presentations, and technical specifications using OCR and NLP techniques
  7. WHEN implementing knowledge graphs THEN the system SHALL create relationships between processes, equipment, materials, defects, and yield impacts using graph databases like Neo4j or Amazon Neptune
  8. WHEN ensuring knowledge freshness THEN the system SHALL implement automated document ingestion pipelines with change detection and version control
  9. IF knowledge base content is updated THEN the system SHALL refresh vector embeddings and update the RAG knowledge base within 24 hours
  10. WHEN handling multilingual content THEN the system SHALL support English, Chinese, Japanese, and Korean technical documentation common in semiconductor manufacturing

Requirement 4: Cross-Functional Team Collaboration

User Story: As a senior manager, I want to ensure engagement from infrastructure, MES, process engineering, and equipment engineering teams, so that the AI ecosystem is adopted and endorsed across all stakeholder groups.

Acceptance Criteria

  1. WHEN infrastructure team participates THEN the system SHALL integrate with existing IT infrastructure and security protocols
  2. WHEN MES team collaborates THEN the system SHALL align with manufacturing execution system workflows
  3. WHEN process engineers engage THEN the system SHALL support process optimization and troubleshooting workflows
  4. WHEN equipment engineers participate THEN the system SHALL provide equipment performance insights and predictive maintenance capabilities
  5. IF stakeholder feedback is provided THEN the system SHALL incorporate feedback into iterative improvements

Requirement 5: Phased Implementation and Deployment

User Story: As a project manager, I want to implement the system in phases with testing and UAT feedback loops, so that we can iterate and improve the solution while minimizing business disruption.

Acceptance Criteria

  1. WHEN planning phases THEN the system SHALL define clear milestones and deliverables for each phase
  2. WHEN deploying to test environments THEN the system SHALL maintain data integrity and system stability
  3. WHEN conducting UAT THEN the system SHALL collect structured feedback from end users
  4. WHEN receiving feedback THEN the system SHALL prioritize and implement improvements in subsequent iterations
  5. IF a phase fails acceptance criteria THEN the system SHALL provide rollback capabilities
  6. WHEN rolling up phases THEN the system SHALL ensure seamless integration between phase deliverables

Requirement 6: Data Traceability and Analytics

User Story: As an engineering data analyst, I want comprehensive data traceability across different dimensions and perspectives, so that I can quickly identify root causes and optimize manufacturing processes for yield improvement.

Acceptance Criteria

  1. WHEN performing wafer-level traceability THEN the system SHALL track individual wafer journey through all process modules including lithography (scanner, track), etch (chamber, recipe), deposition (PVD, CVD, ALD), CMP (pad, slurry), implant (dose, energy), and metrology (measurement results)
  2. WHEN analyzing temporal dimensions THEN the system SHALL correlate time-based patterns including shift effects, tool warm-up periods, consumable aging, and seasonal variations with statistical significance testing
  3. WHEN investigating spatial dimensions THEN the system SHALL analyze within-wafer uniformity, wafer-to-wafer variation, lot-to-lot trends, and fab-wide systematic patterns using spatial correlation algorithms
  4. WHEN examining process dimensions THEN the system SHALL correlate process parameters (temperature, pressure, time, chemistry) with electrical test results and defect densities using multivariate analysis
  5. WHEN evaluating equipment dimensions THEN the system SHALL analyze chamber-to-chamber matching, tool-to-tool variation, and equipment aging effects on process performance
  6. WHEN investigating yield excursions THEN the system SHALL perform automated root cause analysis using decision trees, correlation analysis, and pattern recognition to identify contributing factors
  7. WHEN generating insights THEN the system SHALL provide actionable recommendations including process adjustments, equipment maintenance needs, and recipe optimization suggestions with confidence intervals
  8. WHEN detecting anomalies THEN the system SHALL use statistical process control, machine learning outlier detection, and time-series analysis to identify deviations from normal operation
  9. IF critical anomalies are detected THEN the system SHALL trigger automated alerts to process engineers, equipment engineers, and production supervisors with severity classification
  10. WHEN performing predictive analytics THEN the system SHALL forecast yield trends, equipment failures, and process drift using time-series forecasting and machine learning models

Requirement 7: AI Model Architecture and Deployment

User Story: As an AI engineer, I want to implement a robust AI architecture that supports multiple model types and deployment patterns, so that we can handle diverse semiconductor manufacturing use cases efficiently.

Acceptance Criteria

  1. WHEN implementing the AI architecture THEN the system SHALL support multiple model types including transformer-based LLMs, convolutional neural networks for image analysis, and time-series models for sensor data
  2. WHEN deploying LLM models THEN the system SHALL support both cloud-based (AWS SageMaker, Azure ML, GCP Vertex AI) and on-premises deployment using containers (Docker, Kubernetes)
  3. WHEN implementing RAG architecture THEN the system SHALL use vector databases (Pinecone, Weaviate, Chroma, or FAISS) for efficient similarity search and retrieval
  4. WHEN processing multimodal data THEN the system SHALL handle text (process logs, reports), images (wafer maps, SEM images), time-series (sensor data), and structured data (databases) in unified workflows
  5. WHEN implementing model serving THEN the system SHALL use inference servers (TensorRT, ONNX Runtime, or Triton) for optimized model performance
  6. WHEN ensuring model governance THEN the system SHALL implement MLOps practices including model versioning, A/B testing, performance monitoring, and automated retraining pipelines
  7. WHEN handling real-time inference THEN the system SHALL support streaming data processing with latency requirements under 100ms for critical alerts
  8. WHEN implementing federated learning THEN the system SHALL support distributed training across multiple fab sites while maintaining data privacy
  9. IF model performance degrades THEN the system SHALL automatically trigger model retraining or rollback to previous versions
  10. WHEN scaling inference THEN the system SHALL support auto-scaling based on request volume and computational requirements

Requirement 8: Semiconductor-Specific AI Use Cases

User Story: As a process engineer, I want AI-powered solutions for specific semiconductor manufacturing challenges, so that I can improve yield, reduce cycle time, and optimize process performance.

Acceptance Criteria

  1. WHEN performing yield prediction THEN the system SHALL predict wafer-level and die-level yield based on inline process parameters and metrology data with accuracy >85%
  2. WHEN detecting process excursions THEN the system SHALL identify deviations from normal process behavior using multivariate statistical analysis and machine learning anomaly detection
  3. WHEN optimizing process recipes THEN the system SHALL recommend parameter adjustments to improve yield, uniformity, and throughput using design of experiments (DOE) and Bayesian optimization
  4. WHEN predicting equipment failures THEN the system SHALL forecast maintenance needs based on equipment sensor data, historical failure patterns, and usage statistics
  5. WHEN analyzing defect patterns THEN the system SHALL classify defect types, identify systematic vs. random defects, and correlate defects with process conditions
  6. WHEN performing root cause analysis THEN the system SHALL identify the most likely causes of yield loss or process excursions using causal inference and correlation analysis
  7. WHEN optimizing chamber matching THEN the system SHALL recommend process adjustments to minimize chamber-to-chamber variation and improve uniformity
  8. WHEN analyzing wafer maps THEN the system SHALL detect spatial patterns, systematic signatures, and equipment fingerprints using computer vision and pattern recognition
  9. WHEN supporting new product introduction THEN the system SHALL accelerate ramp-up by transferring learning from similar products and processes
  10. WHEN implementing virtual metrology THEN the system SHALL predict metrology results based on process parameters to reduce measurement overhead and cycle time

Requirement 9: Performance and Scalability

User Story: As an infrastructure engineer, I want the AI system to handle enterprise-scale data volumes and concurrent users, so that it can support mass manufacturing operations without performance degradation.

Acceptance Criteria

  1. WHEN processing large datasets THEN the system SHALL handle petabyte-scale historical data and terabyte-scale daily data ingestion with distributed computing frameworks (Spark, Dask)
  2. WHEN supporting concurrent users THEN the system SHALL handle 100+ simultaneous users with response times under 5 seconds for complex queries
  3. WHEN scaling inference THEN the system SHALL support horizontal scaling using container orchestration (Kubernetes) and load balancing
  4. WHEN processing real-time data THEN the system SHALL handle streaming data rates up to 10,000 events per second from manufacturing equipment
  5. WHEN ensuring high availability THEN the system SHALL maintain 99.9% uptime with redundancy and failover capabilities
  6. IF system load increases THEN the system SHALL automatically scale compute resources and optimize query execution plans
  7. WHEN monitoring performance THEN the system SHALL provide real-time dashboards showing system health, query performance, and resource utilization
  8. WHEN handling peak loads THEN the system SHALL maintain performance during shift changes, lot starts, and batch processing windows
  9. WHEN implementing caching THEN the system SHALL cache frequently accessed data and model predictions to reduce latency
  10. WHEN ensuring data consistency THEN the system SHALL maintain ACID properties for critical manufacturing data while supporting eventual consistency for analytics workloads

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