Quantum-Enhanced AI for Next-Gen Semiconductor Process Control

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]

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