Quantum-Enhanced AI for Next-Gen Semiconductor Process Control
State-of-the-Art: Diffusion Models + Physics-Informed Neural Networks + Causal AI for Fab 2nm Optimization
Skills you will gain:
About Workshop:
Master AI techniques for semiconductor defect detection, process optimization, and yield prediction using Google Colab. This hands-on workshop transforms wafer maps into actionable insights using CNNs, gradient boosting, LSTM autoencoders, and reinforcement learning – complete with industry benchmarks and production-ready code.
Aim: Equip participants with practical ML skills to solve real semiconductor manufacturing challenges – from wafer defect classification to process control optimization using accessible Google Colab environment.
Workshop Objectives:
What you will learn?
DAY 1: Diffusion Models for Wafer Defect Synthesis & Zero-Shot Classification
├── 1.1 Diffusion Model Architecture (Denoising U-Net + DDPM) [15min]
├── 1.2 Training on WM-811K → Generate Synthetic Wafer Maps [25min]
├── 1.3 Zero-Shot Classification via CLIP + Wafer Embeddings [25min]
├── 1.4 Uncertainty Quantification (Monte Carlo Dropout) [15min]
└── 1.5 Real-time Inference Pipeline (<10ms/wafer) [10min]
DAY 2: Physics-Informed Neural Operators for Multi-Scale Process Modeling
├── 2.1 Fourier Neural Operators (FNO) Theory + Implementation [20min]
├── 2.2 PINN Loss: Navier-Stokes + Lithography PDE Constraints [25min]
├── 2.3 Multi-Scale CD Prediction (1nm → 100μm resolution) [25min]
├── 2.4 Operator Learning for Etching Rate Fields [15min]
└── 2.5 Gradient-Based Optimal Control (MPC Framework) [5min]
DAY 3: Causal Discovery + Multi-Agent RL for Adaptive Fab Control
├── 3.1 Causal Graph Discovery (PC Algorithm + NOTEARS) [20min]
├── 3.2 Multi-Agent PPO for Distributed Process Control [25min]
├── 3.3 Counterfactual Analysis: “What-if” Process Scenarios [20min]
├── 3.4 Safe RL with Lagrangian Constraints (2nm tolerance) [15min]
└── 3.5 Online Learning Pipeline (Active Inference) [10min]
Mentor Profile
Fee Plan
Important Dates
21 Jan 2026 Indian Standard Timing 4:30 PM
21 Jan 2026 to 23 Jan 2026 Indian Standard Timing 5:30 PM
Get an e-Certificate of Participation!
CORE FRAMEWORKS:
• PyTorch Lightning (diffusion models, FNO)
• JAX (PINN acceleration, causal discovery)
• Ray RLlib (multi-agent PPO)
• Pyro (Bayesian uncertainty quantification)
ADVANCED LIBRARIES:
• NeuralOperators (FNO2D/3D implementations)
• Diffrax (ODE solvers for diffusion)
• DoWhy (causal inference pipelines)
• Safety Gym (constrained RL environments)
HARDWARE:
• Colab Pro+ (A100 GPU) or local H100
• 48GB RAM minimum for FNO training
Intended For :
✅ Published 2+ papers in ML/AI (NeurIPS/ICLR/IEEE Transactions)
✅ Advanced PyTorch proficiency (custom layers, optimizers)
✅ Semiconductor device physics (quantum transport, band theory)
✅ Numerical PDE solvers experience (FEniCS, FDM/FEM)
✅ Multi-agent systems or causal inference background
IDEAL PROFILE:
“PhD Year 4+, 3+ ML papers, works on 2nm/1.4nm process development
Active GitHub with 100+ stars on ML repos
Attended NeurIPS/ICLR workshops on diffusion models/neural operators
Career Supporting Skills
Workshop Outcomes
1️⃣ DIFFUSION MODELS: Generate unlimited synthetic wafer maps → solve data scarcity
2️⃣ NEURAL OPERATORS: Solve PDE-constrained multi-scale process modeling
3️⃣ CAUSAL AI: Discover true process relationships (not correlations)
4️⃣ MULTI-AGENT RL: Distributed fab-wide optimal control
5️⃣ PINNs: Physics + data-driven process prediction (<1nm accuracy)