State-of-the-Art: Diffusion Models + Physics-Informed Neural Networks + Causal AI for Fab 2nm Optimization
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]
✅ 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
February 2, 2026
IST 04:30 PM
February 2, 2026 – February 4, 2026
IST 05:30 PM
₹2,499
$50
The live sessions of this workshop are over, so the live fees no longer apply. The recordings are being uploaded. They appear in your dashboard as soon as they are ready, and we will e-mail you then.
Yes. Paid participants get the session recordings and study material in their NanoSchool dashboard (My registrations → Recorded Lectures / Study Material).
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Live, mentor-led and certified. Dates and times in IST.
