📚 Syllabus & Course Curriculum
Nanotechnology & Materials Science
Module-by-module breakdown of Quantum-Enhanced AI for Next-Gen Semiconductor Process Control, from foundations to a certified capstone project.
Outline
Implement Denoising U‑Net + DDPM architecture • Generate synthetic wafer maps using WM‑811K dataset • Apply CLIP‑based zero‑shot classification on wafer embeddings • Quantify prediction uncertainty with Monte‑Carlo Dropout
Outline
Code Fourier Neural Operators (FNO) from theory to practice • Embed Navier‑Stokes and lithography PDE constraints via PINN loss • Predict critical dimension (CD) across 1 nm‑100 µm resolution • Learn operator‑based etching‑rate field estimation
Outline
Discover causal graphs using PC algorithm and NOTEARS • Build distributed process controllers with Multi‑Agent PPO • Perform counterfactual ‘what‑if’ scenario analysis • Enforce safety via Lagrangian‑constrained RL (≤2 nm tolerance)
Earn government-registered certification in Quantum-Enhanced AI for Next-Gen Semiconductor Process Control
e-Certificate and e-Marksheet issued on successful completion.
