Advanced Analytics Platform for Fab Operations & Infrastructure Readiness
A production-ready capacity planning and optimization system specifically designed for semiconductor manufacturing, featuring real-time operations monitoring, Monte Carlo risk simulation, linear programming optimization, and interactive dashboards.
This system provides end-to-end capacity management capabilities for semiconductor fabrication facilities, with a focus on:
- Real-time Operations Monitoring: OEE analysis, utilization tracking, and bottleneck identification
- Capacity Planning & Optimization: Linear programming for CapEx allocation, scenario analysis
- Risk Assessment: 10,000-iteration Monte Carlo simulations for capacity risk
- NPI Readiness Tracking: Phase-gate management (EVT/DVT/PVT/MP) and yield learning curves
- Interactive Dashboards: Professional-grade visualizations with 5 analytical views
- Predictive Analytics: Demand forecasting, MTBF analysis, and reliability metrics
# Clone the repository
git clone [https://github.com/QuantuMaster007/Semiconductor_Capacity_Management_System.git](https://github.com/QuantuMaster007/Semiconductor_Capacity_Management_System.git)
cd semiconductor-capacity-system
# Install dependencies
pip install -r requirements.txt# Option 1: Interactive menu
python main.py --menu
# Option 2: Full pipeline (generate data, run analytics, launch dashboard)
python main.py --full
# Option 3: Individual components
python main.py --generate # Generate synthetic data only
python main.py --analyze # Run analytics only
python main.py --dashboard # Launch dashboard only
python main.py --report # Generate summary report onlyOnce launched, open your browser to:
http://localhost:8050
semiconductor-capacity-system/
├── data/
│ ├── raw/ # Generated synthetic fab data
│ │ ├── equipment_master.csv # 161 tool specifications
│ │ ├── fab_operations.csv # 235K+ daily operation records
│ │ ├── demand_forecast.csv # Multi-year demand projections
│ │ ├── capex_projects.csv # $2.5B+ investment portfolio
│ │ └── npi_milestones.csv # 6 NPI programs across 4 phases
│ └── processed/ # Analytics outputs
│
├── models/
│ └── capacity_planning.py # Advanced analytical models
│ ├── CapacityPlanningModel
│ │ ├── Bottleneck Analysis (Theory of Constraints)
│ │ ├── Monte Carlo Simulation (10K iterations)
│ │ ├── Linear Programming Optimization
│ │ └── Scenario Analysis
│ └── ToolReliabilityModel
│ └── MTBF & Availability Analysis
│
├── dashboards/
│ └── interactive_dashboard.py # Professional Dash app
│ ├── Operations Dashboard
│ ├── Capacity Planning
│ ├── CapEx Portfolio
│ ├── NPI Readiness
│ └── Risk Analytics
│
├── data_generator.py # Synthetic data generator
├── main.py # Main orchestrator
├── requirements.txt # Python dependencies
└── README.md # This file
- Real-time OEE trend analysis with component breakdown (Availability, Performance, Quality)
- Tool utilization heatmap by cleanroom bay and category
- Daily output analysis by tool type
- Equipment status monitoring
- Bottleneck analysis using Theory of Constraints
- Utilization forecasting at target output levels
- Multi-scenario capacity planning (Conservative, Base Case, Aggressive, Stretch)
- Quarterly demand trend visualization
- Project timeline with Gantt-style visualization
- Risk-Return scatter matrix (NPV vs Investment)
- IRR analysis with hurdle rate benchmarking
- Portfolio optimization results ($1.5B+ optimized allocation)
- Program progress tracking across EVT/DVT/PVT/MP phases
- Yield learning curves by program
- Infrastructure gate completion matrix
- Phase-specific KPI tracking
- Monte Carlo capacity risk distribution (10,000 simulations)
- Service level probability metrics
- Tool reliability and MTBF analysis
- Scenario-based capacity gap assessment
Uses Theory of Constraints to identify capacity-limiting tool types:
- Calculates utilization at target output (18K wafers/week)
- Accounts for process step complexity by tool type
- Identifies constraint severity and capacity gaps
10,000-iteration simulation varying:
- Demand (±15% volatility)
- Yield (75-98% range, mean 92%)
- Availability (Beta distribution, skewed high)
- Cycle Time (log-normal distribution)
Output metrics:
- Mean/Median/P95/P99 shortfall
- Service level probability
- Capacity at risk (P95)
Maximizes portfolio NPV subject to:
- Budget constraints ($1.5B default)
- Risk-weighted investment limits
- Binary/continuous project selection
Uses scipy.optimize.linprog with HiGHS solver.
Four scenarios (Conservative, Base Case, Aggressive, Stretch) with varying:
- Demand growth rates (5% to 35%)
- Yield assumptions (88% to 95%)
- Capacity gap calculations
- Tool Types: EUV/DUV Lithography, Etch, Deposition (CVD/PVD), CMP, Metrology (SEM/Optical), Ion Implant, Wet Process
- Key Fields: Cost ($1.2M to $180M), Throughput, MTBF, Install Date, Status
- Metrics: OEE, Utilization, Availability, Performance, Quality, WIP, Cycle Time, Downtime
- Time Span: 18 months (Jan 2023 - Jun 2024)
- Granularity: Daily, per tool, with realistic variability
- Products: 6 SKUs (Mobile SoC 3nm/5nm, HPC CPU/GPU 5nm, Automotive 5nm, IoT 7nm)
- Horizon: 5 years quarterly projections
- Revenue: $4.2K to $18K per wafer depending on product
- Projects: 8 major investments (EUV expansions, new capabilities, upgrades)
- Metrics: NPV, IRR, Payback Period, Risk Level, Strategic Priority
- Timeline: 2023-2026
- Programs: 6 new product introductions
- Phases: EVT → DVT → PVT → MP (3-4 months each)
- Tracking: Yield curves, gate completion, infrastructure readiness
- Identify capacity constraints before they impact delivery
- Model demand scenarios and capacity responses
- Optimize CapEx allocation across competing projects
- Track tool reliability and predict maintenance needs
- Monitor NPI readiness across multiple programs
- Track yield learning curves and phase-gate progress
- Align infrastructure readiness with product ramps
- Assess critical path risks
- Portfolio-level financial metrics (NPV, IRR, Payback)
- Risk-adjusted investment decisions
- Strategic capacity roadmap visualization
- ROI analysis and budget utilization tracking
| Category | Technology |
|---|---|
| Language | Python 3.9+ |
| Data Processing | pandas, numpy |
| Optimization | scipy (Linear Programming) |
| Visualization | Plotly, Dash |
| Statistical Analysis | scipy.stats (Monte Carlo) |
| Machine Learning | scikit-learn |
Fleet OEE: 84.3% (↑ 2.3%)
Daily Output: 127,548 wafers
Average Utilization: 85.6%
Active Tools: 161 assets
Portfolio NPV: $640M on $2.5B investment
1. Metrology_SEM: 94.2% utilization → CRITICAL
2. Lithography_EUV: 91.8% utilization → CRITICAL
3. Ion_Implant: 88.5% utilization → Warning
Service Level Probability: 87.3%
Mean Shortfall: 1,247 WPW
P95 Shortfall: 4,892 WPW
Capacity at Risk (P95): 124,156 WPW
python main.py --full# Build image
docker build -t capacity-management .
# Run container
docker run -p 8050:8050 capacity-managementCompatible with:
- Heroku
- AWS Elastic Beanstalk
- Google App Engine
- Azure App Service
Edit config.yaml (optional) to customize:
- Target utilization rates
- Bottleneck thresholds
- CapEx budget constraints
- Simulation parameters
- Risk weights
This is a demonstration project for portfolio. If you'd like to extend it:
- Fork the repository
- Create a feature branch
- Add enhancements (new models, visualizations, data sources)
- Submit a pull request
Let's connect! Whether you have a question or just want to say hi, feel free to reach out.
| Platform | Link |
|---|---|
| 👤 Name | Sourabh Tarodekar |
| ✉️ Email | sourabh232@gmail.com |
| linkedin.com/in/sourabh232 | |
| 🚀 Portfolio | QuantuMaster007 Portfolio |
MIT License - See LICENSE file for details
This system uses synthetic data generated programmatically to demonstrate capacity management concepts for semiconductor manufacturing. The models, algorithms, and visualizations are production-grade, but data is simulated for demonstration purposes.
- Theory of Constraints (Goldratt)
- Semiconductor Manufacturing Handbook (Quirk & Serda)
- Factory Physics (Hopp & Spearman)
- Applied Optimization with MATLAB (Venkataraman)
Built with ❤️ for semiconductor manufacturing excellence