Quantum-Enhanced AI for Next-Gen Semiconductor Process Control

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.

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.

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]

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)

✅ 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