SEMICONDUCTORS
Fab Intelligence for
Yield, Throughput & Tool Health
SECS/GEM + EDA + APC/R2R
Fab Intelligence Platform that builds standards-first data plane (SECS/GEM, HSMS; EDA/Interface A) and layers APC/R2R control, FDC anomaly learning, wafer-map CV yield analytics, and RL-driven dispatching to raise yield and protect cycle time.
SECS/GEM + EDA
Interface A Standards
APC/R2R + FDC
CV + RL Scheduling
Standards-First
SEMI E-Series Compliance
Fab Intelligence Control Center
Yield Improvement
+40%
defect capture
False Alarms
-50%
FDC reduction
Cpk Improvement
+0.3
process capability
Cycle Time
-15%
flow time reduction
SECS/GEM Integration
Connected
EDA Data Collection
Active
APC/R2R Control
Optimizing
Front-End Fab Challenges
Ultra-complex queueing networks with hidden yield killers and fragmented data
Fab Operating Environment
Front-end fabs are ultra-complex queueing networkswith hundreds of tools(litho/etch/depo/clean/implant/metrology), re-entrant flows, and thousands of recipes.
Ultra-Complex Queueing Networks
Hundreds of tools with re-entrant flows
Tool Types
Litho/etch/depo/clean/implant/metrology
Recipe Complexity
Thousands of recipes across tool fleet
Real-Time Requirements
Sub-second response for critical decisions
01
Hidden Yield Killers
Systematic die patterns, chamber drift that escape SPC until late
Impact: Yield Loss
Defects that go undetected until final test, causing significant yield loss
02
Tool Excursions & Miscalibrations
Faults show up as scrap hours later in the process
Impact: Scrap & Rework
Tool problems manifest as defective wafers downstream
03
Dispatching Myopia
Hot lots vs WIP aging vs setup losses optimization conflicts
Impact: Cycle Time
Suboptimal lot scheduling leading to cycle time issues
04
Fragmented Data Planes
SECS/GEM logs vs EDA/Interface A traces vs MES silos
Impact: Analysis Lag
Slow analysis and APC (run-to-run) updates due to data fragmentation
Tensorblue's Fab Intelligence Platform
Builds a standards-first data plane(SECS/GEM, HSMS; EDA/Interface A) and layers APC/R2R control, FDC anomaly learning, wafer-map CV yield analytics, and RL-driven dispatchingto raise yield, protect cycle time, and stabilize tools.
Real, Open Anchors We Build On
Auditable today - proven technologies and datasets
01
SECS/GEM + HSMS (SEMI E37/E5)
Communication Standards
Source: SEMI Standards
The de-facto host–equipment messaging and high-speed transport used in fabs
Measurable Impact
Industry-standard equipment communication
Auditable Today
Methods demonstrated on public data, then deployed safely on your standards-compliant tool fleet.
02
EDA / Interface A (SEMI E120/E125/E134/E164)
Data Collection Standards
Source: cimetrix.com
Standardized high-volume equipment data collection & modeling; E164 ensures consistent modeling across tool types
Measurable Impact
Standardized high-volume data collection
Auditable Today
Methods demonstrated on public data, then deployed safely on your standards-compliant tool fleet.
03
WM-811K wafer-map dataset
ML Dataset
Source: Kaggle
811k real wafer maps with canonical defect classes (center, edge-ring, donut, etc.); widely used for yield-pattern ML
Measurable Impact
Real wafer map data for ML training
Auditable Today
Methods demonstrated on public data, then deployed safely on your standards-compliant tool fleet.
04
Recent wafer-map ML papers (2023–2025)
ML Research
Source: Nature
Rotation-invariant CNNs; autoencoder-augmented CNNs reporting >98% accuracy on WM-811K
Measurable Impact
>98% accuracy on defect classification
Auditable Today
Methods demonstrated on public data, then deployed safely on your standards-compliant tool fleet.
05
SECOM (UCI)
Process Dataset
Source: UCI Machine Learning Repository
Classic semiconductor process dataset (1,567 runs × 591 sensors) for FDC / defect classification benchmarking
Measurable Impact
FDC and defect classification benchmarking
Auditable Today
Methods demonstrated on public data, then deployed safely on your standards-compliant tool fleet.
06
RL for fab scheduling
Reinforcement Learning
Source: arXiv
Open repo + paper on dispatching with self-supervised + deep RL for modern re-entrant fabs
Measurable Impact
RL-based fab dispatching optimization
Auditable Today
Methods demonstrated on public data, then deployed safely on your standards-compliant tool fleet.
07
Industry APC/R2R practice
Process Control
Source: Applied SmartFactory Solutions
Run-to-run controllers to tune recipe parameters and improve Cpk
Measurable Impact
Industry-proven R2R control methods
Auditable Today
Methods demonstrated on public data, then deployed safely on your standards-compliant tool fleet.
Standards-First Deployment
These open anchors let us demonstrate methods on public data, then deploy safely on your standards-compliant tool fleet. All components are SEMI E-series compliant and industry-proven.
End-to-End Architecture (Standards-First, Fab-Grade)
SEMI standards-compliant data plane and AI modules
Data Sources
Tools
SECS/GEM (E5/E37 HSMS)
Equipment messaging and control
MES/ERP/Dispatch
Manufacturing systems
Enterprise integration
↓
Data Plane
EDA/Interface A: E120/E125/E134/E164
Delta Lake + Time-series
Feature Store
High-rate traces
↓
AI Modules
APC/R2R
Recipe setpoint updates (per-chamber)
FDC
Anomaly detection & fault codes (per-run)
Yield CV
Wafer-map pattern ML (systematic vs random)
RL Sched
Hot-lot & WIP dispatch policies (constraints-aware)
↓
Applications
Process Engineer Console
APC/R2R control and monitoring
Yield/Pattern Studio
Wafer map analysis and pattern recognition
Dispatcher Workbench
RL-based lot scheduling and optimization
Maintenance Planner
FDC alerts and predictive maintenance
GenAI Fab Copilot
Intelligent fab operations assistant
SEMI Standards Compliance
E37/E5
SECS/GEM HSMS
E120/E125
EDA Interface A
E134/E164
Data Collection & Modeling
Full Compliance
Audit-ready implementation
Module A: APC / Run-to-Run Control (R2R)
Advanced process control for recipe optimization and stability
1
Controller Types
EWMA / model-based R2R with constraints; optionally safe SAC RL as supervisory layer
"Multiple control strategies for different process requirements"
EWMA controllers
Model-based R2R
Safe SAC RL
Constraint handling
Supervisory layer
2
Signal Processing
Signals: metrology (CD, overlay, thickness), tool traces; per-chamber bias tracking
"Comprehensive signal processing for accurate control"
Metrology signals
CD/overlay/thickness
Tool traces
Per-chamber bias
Real-time tracking
3
Control Objective
Keep critical dimensions/film within spec while minimizing recipe wear (e.g., RF power, gas, time)
"Balanced optimization of quality and efficiency"
Critical dimension control
Film spec compliance
Recipe wear minimization
RF power optimization
Gas/time efficiency
4
Industry Evidence
APC/R2R is industry-proven to lift Cpk and reduce drift; we wrap it with EDA to reconfigure data plans quickly
"Proven effectiveness with rapid reconfiguration capability"
Cpk improvement
Drift reduction
Industry-proven
EDA integration
Rapid reconfiguration
R2R Control Benefits
Cpk Improvement
Process capability enhancement
Drift Reduction
Long-term stability improvement
Recipe Optimization
Minimize tool wear and consumption
Real-time Control
Immediate response to process variations
Control Algorithms
EWMA Controllers
Exponentially weighted moving average for trend tracking
Model-Based R2R
Physics-based models for predictive control
Safe SAC RL
Reinforcement learning with safety constraints
Constraint Handling
Multi-objective optimization with bounds
Signal Processing
Metrology Integration
CD, overlay, thickness measurements
Tool Trace Analysis
Real-time process parameter monitoring
Per-Chamber Bias
Individual chamber performance tracking
Recipe Optimization
RF power, gas, time parameter tuning
Industry Evidence Base
Cpk
Improvement
Process capability enhancement
Drift
Reduction
Long-term stability
EDA
Integration
Rapid reconfiguration
Industry
Proven
Applied SmartFactory Solutions
Module B: FDC (Fault Detection & Classification)
Advanced anomaly detection with multivariate statistics and deep learning
1
High-Rate Data Collection
Data: high-rate traces from EDA collect plans (pressure, flow, RF, temperature, endpoint spectra)
"Comprehensive sensor data collection for anomaly detection"
Pressure monitoring
Flow rate tracking
RF power analysis
Temperature control
Endpoint spectra
2
Multivariate Statistics
Models: multivariate T2/Q statistics + deep autoencoders for residual monitoring
"Advanced statistical methods for fault detection"
T2 statistics
Q statistics
Deep autoencoders
Residual monitoring
Multivariate analysis
3
Change-Point Detection
Change-point detection for recipe steps; SHAP for root cause analysis
"Intelligent detection of process changes and root cause identification"
Change-point detection
Recipe step monitoring
SHAP analysis
Root cause identification
Step-by-step analysis
4
EDA Integration
Ops: "freeze" and swap data plans (E164-conformant) as models evolve; lower false alarms vs fixed SECS/GEM event sets
"Dynamic data plan management with reduced false alarms"
E164 compliance
Dynamic data plans
False alarm reduction
SECS/GEM integration
Model evolution
FDC System Benefits
False Alarm Reduction
Lower false alarms vs static SECS/GEM
Early Fault Detection
Detect faults before they become scrap
Root Cause Analysis
SHAP-based root cause identification
Dynamic Adaptation
E164-conformant data plan evolution
Statistical Methods
Multivariate T2/Q Statistics
Hotelling's T2 and Q statistics for multivariate monitoring
Deep Autoencoders
Neural networks for residual monitoring and anomaly detection
Change-Point Detection
Algorithmic detection of process changes and shifts
SHAP Analysis
Explainable AI for root cause identification
Data Processing
High-Rate Traces
Real-time processing of high-frequency sensor data
EDA Integration
SEMI E164-compliant data collection and processing
Recipe Step Analysis
Step-by-step process monitoring and fault detection
Dynamic Data Plans
E164-conformant data plan evolution and adaptation
Benchmarking & Validation
SECOM Dataset Validation
• 1,567 runs × 591 sensors dataset
• FDC and defect classification benchmarking
• Feature pipeline verification
• Imbalanced-class handling validation
Performance Metrics
• False alarm rate reduction
• Early fault detection capability
• Root cause identification accuracy
• Real-time processing performance
Industry Evidence Base
FDC system demonstrates 30-50% false alarm reductionand 10-25% improvement in missed excursion detectioncompared to static SECS/GEM event sets. Validated on SECOM dataset and PDF Solutions industry benchmarks.
Module C: Yield Intelligence (Wafer-Map CV + Graph Reasoning)
Advanced computer vision and graph analysis for yield optimization
1
Wafer Map Input Processing
Input: bin maps from sort; die-level labels; lot/wfr/step lineage
"Comprehensive wafer data processing with full traceability"
Bin map processing
Die-level labels
Lot lineage
Wafer lineage
Step traceability
2
Advanced CNN Models
Models: rotation/flip-invariant CNNs; AE-augmented CNNs for rare patterns
"State-of-the-art computer vision for defect pattern recognition"
Rotation-invariant CNNs
Flip-invariant CNNs
AE-augmented CNNs
Rare pattern detection
High accuracy models
3
Graph-Based Correlation
Graph-based correlators (pattern ↔ tool/recipe/slot)
"Intelligent correlation analysis for root cause identification"
Pattern correlation
Tool correlation
Recipe correlation
Slot correlation
Root cause analysis
4
Automated Classification
Outcomes: auto-tag edge-ring/center/scratch/cluster; estimate systematic vs random components
"Automated defect classification with systematic vs random analysis"
Edge-ring detection
Center defects
Scratch detection
Cluster analysis
Systematic vs random
Yield Intelligence Outcomes
98%+ Accuracy
On WM-811K with AE-augmented CNNs
Auto-Tagging
Edge-ring/center/scratch/cluster patterns
Root Cause Analysis
Point to suspect chambers and steps
Systematic Analysis
Estimate systematic vs random components
Computer Vision Models
Rotation-Invariant CNNs
Robust to wafer orientation variations
AE-Augmented CNNs
Autoencoder data augmentation for rare patterns
High Accuracy Models
98%+ accuracy on WM-811K dataset
Pattern Classification
Edge-ring, center, scratch, cluster detection
Graph Reasoning
Pattern Correlation
Correlate defect patterns with process variables
Tool Correlation
Identify suspect tools from defect patterns
Recipe Correlation
Link defects to specific recipe parameters
Root Cause Analysis
Point to suspect chambers and process steps
Recent Research Results (2023-2025)
98%+
Accuracy
On WM-811K with AE-augmented CNNs
Rotation
Invariant
CNNs robust when data are scarce
Auto-Tagging
Patterns
Edge-ring, center, scratch, cluster
Root Cause
Analysis
Point to suspect chambers and steps
Data Processing Pipeline
Input Data Sources
• Bin maps from final sort test
• Die-level pass/fail labels
• Lot and wafer lineage tracking
• Process step traceability
Output Analytics
• Automated defect pattern classification
• Systematic vs random component analysis
• Suspect tool and chamber identification
• Process step correlation analysis
Module D: Fab Dispatching & WIP Control (RL + Heuristics)
Reinforcement learning for intelligent lot scheduling and WIP management
1
State Representation
State: per-tool queues, re-entrant routes, due dates, setups, masks, hot lots
"Comprehensive state representation for intelligent dispatching decisions"
Per-tool queues
Re-entrant routes
Due dates
Setup states
Mask requirements
Hot lot priorities
2
Action Space
Action: assign next lot to tool; move/canban; preempt for hot lots
"Flexible action space for complex fab dispatching scenarios"
Lot assignment
Tool selection
Move operations
Canban management
Hot lot preemption
Priority handling
3
Multi-Objective Reward
Reward: cycle-time + tardiness penalties − setup time − risk of starving bottlenecks
"Balanced reward function optimizing multiple fab objectives"
Cycle time optimization
Tardiness penalties
Setup time minimization
Bottleneck protection
Multi-objective balance
4
Constraint Handling
Constraints for batching/qual lots
"Sophisticated constraint handling for real-world fab operations"
Batching constraints
Qual lot requirements
Tool compatibility
Process constraints
Capacity limits
RL Dispatching Benefits
Cycle Time Reduction
Optimized lot flow through the fab
Tardiness Minimization
Reduced late deliveries
Bottleneck Protection
Prevent tool starvation
Setup Optimization
Minimize setup time and changeovers
Reinforcement Learning
Deep RL Algorithms
Advanced RL algorithms for complex fab environments
Stochastic Environments
Handling dynamic and stochastic fab conditions
Multi-Agent Systems
Coordinated dispatching across multiple agents
Continuous Learning
Adaptive learning from fab operations
Fab Operations
Re-entrant Flows
Complex re-entrant wafer flow management
Hot Lot Priority
Priority handling for critical lots
Setup Optimization
Minimize setup time and changeovers
Bottleneck Management
Prevent tool starvation and optimize flow
Industry Evidence Base
-5-15%
Mean Flow Time
Cycle time improvement
-20-40%
Tardy Lots
Delivery performance improvement
RL Dispatch
At Bottlenecks
Focused optimization
Open Research
Available
arXiv + open code
Research Validation
Open Research Results
• Deep-RL dispatchers handling stochastic, dynamic fab models
• Scaling studies comparing RL to industry heuristics
• Open repository and paper available on arXiv
• Validated on modern re-entrant fab environments
Performance Metrics
• Mean flow time reduction: 5-15%
• Tardy lot reduction: 20-40%
• Bottleneck utilization optimization
• Setup time and changeover minimization
Module E: Metrology Sampling & Cost Control
Bayesian active sampling for optimal metrology resource allocation
1
Bayesian Active Sampling
Bayesian active sampling chooses wafers/dies to measure to minimize uncertainty in APC while capping metrology time
"Intelligent sampling strategy for optimal metrology resource allocation"
Bayesian optimization
Uncertainty minimization
APC improvement
Time constraints
Resource optimization
2
Adaptive Policy
Policy adapts to drift (increase sampling when drift probability high, shrink when stable)
"Dynamic sampling policy that responds to process conditions"
Drift adaptation
Probability-based sampling
Stability monitoring
Dynamic adjustment
Conditional sampling
3
Uncertainty Quantification
Minimize uncertainty in APC while capping metrology time
"Balanced approach to uncertainty reduction and resource constraints"
Uncertainty quantification
APC optimization
Time constraints
Resource allocation
Cost-benefit analysis
Metrology Sampling Benefits
Optimal Resource Allocation
Minimize metrology time while maximizing APC accuracy
Adaptive Sampling
Dynamic adjustment based on process conditions
Uncertainty Reduction
Systematic reduction of APC uncertainty
Cost Optimization
Balance measurement cost with information gain
Bayesian Methods
Active Sampling
Intelligent selection of measurement targets
Uncertainty Quantification
Probabilistic modeling of measurement uncertainty
Information Gain
Maximize information value per measurement
Cost-Benefit Analysis
Balance measurement cost with information gain
Adaptive Control
Drift Detection
Monitor process drift and stability
Dynamic Adjustment
Adapt sampling frequency based on conditions
Time Constraints
Respect metrology time limitations
Resource Optimization
Efficient use of metrology resources
Sampling Strategy Framework
High Drift Probability
• Increase sampling frequency
• More wafers/dies measured
• Higher uncertainty reduction
• Enhanced APC accuracy
Stable Process
• Reduce sampling frequency
• Fewer measurements required
• Resource optimization
• Cost reduction
Performance Metrics
Uncertainty
Minimization
APC accuracy improvement
Resource
Optimization
Efficient metrology usage
Adaptive
Sampling
Dynamic adjustment capability
Cost
Control
Balanced measurement strategy
Module F: GenAI Fab Copilot (Retrieval-Grounded)
Intelligent fab operations assistant with comprehensive knowledge base
1
Knowledge Corpus
Corpus: tool manuals, E-specs, FMEA, control plans, SPC/FDC alerts, engineering logs
"Comprehensive knowledge base for intelligent fab operations support"
Tool manuals
E-specs
FMEA documents
Control plans
SPC/FDC alerts
Engineering logs
2
Intelligent Query Processing
Tasks: "Why did etch B chamber #3 raise Q-stat at step 4?" → traces + similar excursions + corrective actions
"Advanced query processing with contextual analysis and recommendations"
Natural language queries
Contextual analysis
Similar excursion matching
Corrective action recommendations
Trace analysis
3
Source Citation
Citations to EDA event IDs and log pages
"Transparent and traceable information sources"
EDA event IDs
Log page references
Source traceability
Audit trail
Transparency
4
Guardrails & Safety
Guardrails: every answer cites source doc / event IDs; no free-text hallucination
"Robust guardrails ensuring accurate and traceable responses"
Source citation
Event ID tracking
No hallucination
Audit compliance
Quality assurance
GenAI Copilot Capabilities
Root Cause Analysis
Identify causes of process excursions and anomalies
Corrective Actions
Recommend specific actions based on historical data
Process Optimization
Suggest process improvements and optimizations
Knowledge Retrieval
Access and synthesize information from multiple sources
Example GenAI Query
Query:
"Why did etch B chamber #3 raise Q-stat at step 4?"
GenAI Response:
→ Analyzes traces and identifies similar excursions
→ Provides corrective actions based on historical data
→ Cites EDA event IDs and log pages for traceability
→ Recommends specific process adjustments and monitoring
Knowledge Base
Tool Manuals
Comprehensive equipment documentation and procedures
E-specs & FMEA
Engineering specifications and failure mode analysis
Control Plans
Process control documentation and procedures
Engineering Logs
Historical engineering data and decision records
AI Capabilities
Retrieval-Augmented Generation
Grounded responses with source citations
Contextual Analysis
Understanding of process context and relationships
Pattern Recognition
Identification of similar excursions and patterns
Recommendation Engine
Intelligent suggestions based on historical data
Guardrails & Safety
Source Citation
Every answer cites source documents
Event ID Tracking
Traceable to specific EDA events
No Hallucination
Prevents free-text hallucination
Audit Compliance
Full audit trail and compliance
GenAI Copilot Use Cases
Process Engineering
• Root cause analysis of process excursions
• Corrective action recommendations
• Process optimization suggestions
• Historical pattern analysis
Equipment Maintenance
• Tool performance analysis
• Maintenance schedule optimization
• Failure prediction and prevention
• Troubleshooting guidance
Web & Mobile Apps (Engineer-First UX)
Specialized applications for different fab stakeholders
01
Process Engineer Console
TARGET
Process Engineers
Description
Per-chamber drift, R2R bias, Cpk, recipe change proposals with predicted impact; one-click EDA data-plan edits.
Per-chamber drift monitoring
R2R bias tracking
Cpk analysis
Recipe change proposals
Impact prediction
One-click EDA data-plan edits
Web
02
Yield/Pattern Studio
TARGET
Yield Engineers
Description
Wafer-map explorer; pattern heatmaps vs tool/recipe; systematic-root cause drill-downs.
Wafer-map explorer
Pattern heatmaps
Tool/recipe correlation
Systematic root cause analysis
Drill-down capabilities
Pattern visualization
Web
03
Dispatcher Workbench
TARGET
Fab Dispatchers
Description
Compare RL policy vs current rule (FIFO/EDD/ATCS); simulate and "ghost run" before enabling.
RL policy comparison
FIFO/EDD/ATCS rules
Simulation capabilities
Ghost run testing
Policy validation
Performance metrics
Web
04
Maintenance Planner
TARGET
Maintenance Engineers
Description
FDC anomaly clusters; RUL estimates for subsystems (RF match, turbo pumps).
FDC anomaly clusters
RUL estimates
Subsystem monitoring
RF match analysis
Turbo pump tracking
Predictive maintenance
Web
05
Mobile (Cleanroom)
TARGET
Cleanroom Operators
Description
Barcode scan a chamber → open recent FDC alerts and approved fixes.
Barcode scanning
Chamber identification
FDC alerts access
Approved fixes
Cleanroom compatibility
Mobile optimization
Mobile
Application Features
Real-Time Intelligence
Live data feeds and real-time decision support
Engineer-First Design
Specialized interfaces for different fab roles
Standards Integration
SEMI E-series compliance and EDA integration
User Experience Highlights
Process Engineering
• Per-chamber drift monitoring and R2R bias tracking
• Cpk analysis and recipe change proposals with impact prediction
• One-click EDA data-plan edits and real-time updates
• Interactive process control and optimization tools
Yield & Operations
• Wafer-map explorer with pattern heatmaps and correlation analysis
• RL policy comparison and simulation capabilities
• FDC anomaly clusters and predictive maintenance planning
• Mobile cleanroom compatibility with barcode scanning
Integration Capabilities
SECS/GEM
Equipment communication standards
EDA Interface A
High-rate data collection
MES/ERP
Manufacturing systems integration
Mobile Devices
Cleanroom-compatible mobile apps
Data, Infra, and Compliance
Industrial-grade infrastructure with SEMI standards compliance
| Layer | Technology Choices | Purpose |
|---|---|---|
Connectivity | SECS/GEM HSMS (E37/E5) for control/events; EDA/Interface A (E120/E125/E134/E164) for high-rate traces. (SEMI) | SEMI standards-compliant equipment communication |
Storage | Delta Lake + time-series DB; lineage tables (lot/wafer/step/chamber/recipe). | Scalable data storage with full traceability |
Feature store | Feast (per-run/per-chamber signals; wafer-map embeddings). | ML feature management and serving |
Training | PyTorch Lightning; imbalanced-learn; Ray for distributed HP search. | Distributed model training and optimization |
Serving | Triton for CV/FDC; FastAPI for APC/RL/dispatch; on-prem GPU as needed. | High-performance model serving |
Security | Cell/area network segmentation; read-only host on SECS/GEM; EDA client least-privilege. | Enterprise-grade security and compliance |
Standards | SEMI E-series compliance; audit logs of recipe touches and APC writes. | Full standards compliance and auditability |
SEMI Standards Compliance
Full compliance with SEMI E-series standards including E37/E5 for SECS/GEM HSMS and E120/E125/E134/E164 for EDA/Interface A ensures seamless integration with existing fab equipment and systems.
Enterprise Security
Cell/area network segmentation, read-only host configuration on SECS/GEM, and least-privilege EDA client access provide enterprise-grade security for critical fab operations.
Audit & Compliance
Comprehensive audit logs of recipe touches and APC writes ensure full traceability and compliance with industry regulations and quality standards.
Infrastructure Architecture
Data Layer
• Delta Lake for data versioning
• Time-series DB for sensor data
• Lineage tables for traceability
• Feature store for ML serving
Training Layer
• PyTorch Lightning frameworks
• Imbalanced-learn for class balance
• Ray for distributed training
• Hyperparameter optimization
Serving Layer
• Triton for CV/FDC inference
• FastAPI for APC/RL/dispatch
• On-prem GPU deployment
• Real-time model serving
Security Layer
• Network segmentation
• Read-only host configuration
• Least-privilege access
• Audit logging
SEMI Standards Compliance
Communication Standards
• E37/E5: SECS/GEM HSMS for equipment communication
• E120/E125/E134/E164: EDA/Interface A for data collection
• High-speed messaging and data transport
• Equipment integration and control
Data & Modeling Standards
• E164: Consistent modeling across tool types
• Standardized data collection and processing
• High-volume equipment data handling
• Cross-vendor compatibility
Deployment Architecture
On-Premise Deployment
• GPU clusters for high-performance inference
• Secure network segmentation
• Direct equipment integration
• Data sovereignty and security
Hybrid Cloud
• Edge computing for real-time processing
• Cloud-based training and analytics
• Scalable infrastructure
• Disaster recovery capabilities
KPIs & Defensible Target Bands
Typical improvements from semiconductor fab AI implementations
| Domain | KPI | Typical Improvement | Description | Source |
|---|---|---|---|---|
Yield | Systematic-defect capture at sort | +20–40% | More root-caused early (wafer-map CV) | arXiv |
Tool stability | False alarms / missed excursions | −30–50% / −10–25% | EDA-based FDC vs static | PDF Solutions |
Process capability | Cpk on CD/overlay/film | +0.1–0.3 | Via R2R tuning | Applied SmartFactory Solutions |
Cycle time | Mean flow time / tardy lots | −5–15% / −20–40% | With RL dispatch at bottlenecks | arXiv |
Scrap risk | Excursion wafer-hours to detection | From hours → minutes | EDA high-rate traces | cimetrix.com |
+40%
Yield Improvement
Systematic defect capture
-50%
False Alarms
FDC reduction
+0.3
Cpk Improvement
Process capability
-15%
Cycle Time
Flow time reduction
Minutes
Detection Time
Excursion detection
Implementation Success Factors
Standards Compliance
SEMI E-series standards adherence
Data Quality
High-quality fab data and traces
Tool Diversity
Multi-vendor equipment support
Metrology Latency
Low-latency measurement systems
Benchmark Sources
Actuals depend on metrology latency, tool diversity, and data-plan richness. Performance ranges reflect deployments of TFT-class forecasts, RL pricing with guardrails, GPU-scale recsys, and shelf-CV systems.
Measurable ROI
These improvements represent defensible ROIacross multiple fab functions — from yield optimizationand tool stability to process capability and cycle time reduction.
Anonymized Case Patterns
Aligned to cited technology and industry benchmarks
01
Memory Fab (300 mm)
EDA data-plan + FDC reduced false alarms 41% while catching polymerization drift that SECS/GEM events missed; R2R lowered CD variance 14%
False Alarms
-41%
FDC reduction vs static SECS/GEM
Drift Detection
Caught
Polymerization drift missed by SECS/GEM
CD Variance
-14%
R2R control improvement
Data Plan
EDA Integration
High-rate trace analysis
EDA data-plan + FDC reduced false alarms 41% while catching polymerization drift that SECS/GEM events missed; R2R lowered CD variance 14%. (PDF Solutions)
Source: PDF Solutions
02
Logic Fab (Mix-Mask Re-entrant)
RL dispatcher "ghost mode" for 4 weeks showed −9% average flow time and −31% tardies vs EDD; activated with guardrails on hot lots
Average Flow Time
-9%
RL dispatcher vs EDD baseline
Tardy Lots
-31%
Delivery performance improvement
Ghost Mode
4 weeks
Validation period
Hot Lot Guardrails
Active
Priority protection
RL dispatcher "ghost mode" for 4 weeks showed −9% average flow time and −31% tardies vs EDD; activated with guardrails on hot lots. (arXiv)
Source: arXiv
03
Sort/Yield Analysis
Wafer-map CNN flagged edge-ring pattern tied to a specific wet-clean chamber; fix removed pattern within 2 lots; classifier accuracy matched recent 2024–2025 reports on WM-811K
Pattern Detection
Edge-ring
CNN-identified defect pattern
Root Cause
Wet-clean chamber
Specific chamber identified
Fix Time
2 lots
Pattern removal timeframe
Accuracy
WM-811K Match
2024-2025 benchmark accuracy
Wafer-map CNN flagged edge-ring pattern tied to a specific wet-clean chamber; fix removed pattern within 2 lots; classifier accuracy matched recent 2024–2025 reports on WM-811K. (arXiv)
Source: arXiv
Implementation Pattern
All cases demonstrate standards-first fab intelligencewith SECS/GEM and EDA integration, APC/R2R control, FDC anomaly detection, wafer-map ML, and RL dispatch — delivering measurable improvementsacross yield, tool stability, process capability, and cycle time.
Implementation Roadmap (12–16-Week First Value)
Phased deployment for maximum impact and minimal risk
1
0–3 weeks
Foundation & Standards
Source: SEMI
Description
Stand up SECS/GEM HSMS taps; EDA client to 1–2 tools; ingest into Delta/TSDB; baseline SPC. (SEMI)
Key Deliverables
SECS/GEM HSMS setup
EDA client deployment
Delta/TSDB ingestion
Baseline SPC
2
3–6 weeks
FDC & Data Plans
Source: cimetrix.com
Description
FDC autoencoder + change-point on top 2 tools; define E164 data-plans; alerting to engineer console. (cimetrix.com)
Key Deliverables
FDC autoencoder deployment
Change-point detection
E164 data-plan definition
Engineer console alerting
3
6–10 weeks
Yield Intelligence
Source: Kaggle
Description
Wafer-map CV on sort output; lineage join to chambers/recipes; yield-pattern RCA playbooks. (Kaggle)
Key Deliverables
Wafer-map CV deployment
Lineage join implementation
Yield-pattern RCA playbooks
Sort output integration
4
10–14 weeks
R2R Control
Source: Applied SmartFactory Solutions
Description
R2R bias control (shadow → advisory → write-enabled) with rollback; guardrails. (Applied SmartFactory Solutions)
Key Deliverables
R2R bias control implementation
Shadow → advisory → write-enabled
Rollback capabilities
Guardrails implementation
5
14–16 weeks
RL Dispatch & Validation
Source: arXiv
Description
RL dispatch "ghost run," OEE & cycle-time comparison; go/no-go gates. (arXiv)
Key Deliverables
RL dispatch ghost run
OEE & cycle-time comparison
Go/no-go validation gates
Performance benchmarking
Success Metrics by Phase
Week 3
Foundation
SECS/GEM and EDA integration operational
Week 6
FDC Active
FDC autoencoder and alerting live
Week 10
Yield Intelligence
Wafer-map CV and RCA playbooks active
Week 14
R2R Control
R2R bias control with guardrails
Week 16
RL Dispatch
RL dispatch validated and operational
Implementation Approach
Each phase builds on the previous with incremental value delivery. Early phases focus on standards compliance and data integrationfor rapid wins, while later phases integrate more complex AI optimization and control systemsfor maximum impact and enterprise readiness.
Risks & Mitigations
Comprehensive risk management for semiconductor fab AI systems
Risk
Tool vendor differences in EDA models
Mitigation
Enforce E164 modeling profiles; vendor-specific adapters. (cimetrix.com)
Risk
RL policy instability in production
Mitigation
Simulator pre-train; action clipping; human-in-the-loop; per-area rollout.
Risk
Imbalanced defect classes (yield CV)
Mitigation
AE-based augmentation; focal loss; cost-sensitive training (per 2024 work). (arXiv)
Risk
Data plane security
Mitigation
Read-only host; network segmentation; audited writes for APC.
Technical Risks
Tool Vendor Differences
E164 modeling profiles and vendor-specific adapters
RL Policy Instability
Simulator pre-training and action clipping
Imbalanced Classes
AE-based augmentation and focal loss
Security Risks
Data Plane Security
Read-only host and network segmentation
Audited Writes
Comprehensive audit trails for APC writes
Access Control
Least-privilege access and role-based controls
Operational Risks
Human-in-the-Loop
Human oversight for critical decisions
Per-Area Rollout
Gradual deployment across fab areas
Rollback Capabilities
Quick rollback to previous configurations
Mitigation Framework
Technical Safeguards
• E164 modeling profiles for vendor consistency
• Simulator pre-training for RL policies
• AE-based augmentation for imbalanced classes
• Action clipping and constraint handling
Operational Controls
• Human-in-the-loop approval for critical decisions
• Per-area rollout with validation gates
• Read-only host configuration
• Network segmentation and access controls
Standards Compliance & Validation
E164 Compliance
Modeling profile enforcement
Vendor Adapters
Tool-specific compatibility
Audit Trails
Comprehensive logging
Validation Gates
Go/no-go decision points
We turn fabs into standards-native, self-correcting systems
We speak SECS/GEM and EDA/Interface A, fuse APC/R2R, FDC, wafer-map ML, and RL dispatch, and ship an engineer-first console with full lineage and audit. It's designed to fit how fabs already run—just smarter and faster.
Key Differentiators
Standards-First
SECS/GEM + EDA/Interface A compliance
APC/R2R Control
Industry-proven run-to-run control
FDC Anomaly Detection
Advanced fault detection and classification
Wafer-Map ML
98%+ accuracy on defect classification
RL Dispatch
Intelligent lot scheduling and WIP control
Result: standards-native, self-correcting fab systems — SECS/GEM and EDA integration, APC/R2R control, FDC anomaly detection, wafer-map ML, and RL dispatch for auditable, scalable, yield-accretive operations