Sale!
Machine Learning for IC Yield: Models, SHAP Explainability & APC/R2R
Original price was: INR ₹11,000.00.INR ₹5,499.00Current price is: INR ₹5,499.00.
From fab data to higher yield—predict, optimize, and control Join NanoSchool (NSTC) and get certified with practical industry standards Join NanoSchool (NSTC) and get certified with practical industry standards. Enroll now with NanoSchool (NSTC) to get certified through industry-ready, professional learning built for practical outcomes and career growth.
About This Course
This 3-day, hands-on course is designed to help participants turn fab/process data into higher wafer yields by leveraging leakage-safe machine learning (ML), explainability techniques (such as SHAP), and Run-to-Run (R2R) control/Advanced Process Control (APC). Participants will build and evaluate predictors, prototype virtual metrology, and leave with a practical, data-backed plan to improve wafer yield.
Aim
The course will empower participants to convert fab data into measurable yield gains using cutting-edge ML, explainability methods, and R2R/APC techniques.
Course Objectives
- Learn the fundamentals of IC yield and baseline analytics.
- Apply leakage-safe ML to predict yield effectively.
- Gain hands-on experience in feature engineering and model evaluation.
- Optimize processes using R2R/APC techniques and virtual metrology.
- Interpret yield drivers and develop actionable yield improvement plans.
- Understand MLOps, real-time monitoring, and deployment in fab environments.
Course Structure
📅 Module 1 – Foundations: Yield and Process Data Analytics
- IC Yield Basics: Gain a solid understanding of yield types and their impact on production costs.
- Process Data Overview: Explore key data sources (MES, SPC, wafer maps) and the challenges related to data quality.
- Baseline Analytics for Yield: Use classical defect models and loss analysis tools to analyze yield.
- Hands-On: Engage in dataset creation, basic analysis, and yield estimation exercises.
📅 Module 2 – Machine Learning for Yield Prediction
- Feature Engineering & Data Preparation: Learn how to handle data imbalances and define relevant features for prediction models.
- ML Models for Yield Prediction: Dive into regression and classification techniques tailored for yield prediction.
- Model Evaluation & Interpretability: Explore key metrics and explainability tools like SHAP to interpret model outputs.
- Hands-On: Build your own yield prediction model and learn to interpret the results effectively.
📅 Module 3 – Optimization and Real-time Control
- Run-to-Run (R2R) Control & APC: Use machine learning to optimize production parameters and enhance yield.
- Process Parameter Optimization: Apply Bayesian optimization techniques to improve yield.
- MLOps & Digital Twins: Learn how to deploy models in real-time environments for continuous monitoring.
- Hands-On: Apply R2R control, simulate optimization techniques, and present strategies for yield improvement.
Course Outcomes
- Build and evaluate ML models for yield prediction.
- Apply SPC, Pareto, and classical models for yield analysis.
- Optimize processes using R2R/APC and virtual metrology.
- Implement feature engineering and time-series models.
- Interpret yield drivers using SHAP and permutation importance.
- Develop actionable yield-improvement plans.
- Gain insights into MLOps and best practices for fab data.
Who Should Enrol?
- PhD scholars and postgraduate students in microelectronics, VLSI, materials science, or data science.
- Academicians and researchers involved in semiconductor manufacturing or ML.
- Fab professionals: process/yield/device/test/metrology/equipment engineers, DFM/PDK, MES/IT, quality/OEE engineers.
- Data scientists and ML engineers supporting fab analytics, virtual metrology (VM), or APC/R2R systems.
| Brand | NSTC |
|---|---|
| Format | Online (e-LMS) |
| Duration | 3 Weeks |
| Level | Advanced |
| Domain | AI, Data Science, Automation, Artificial Intelligence |
| Hands-On | Yes – Practical projects with industrial datasets |
| Tools Used | Python, TensorFlow, Power BI, MLflow, ML Frameworks, Computer Vision |
Related products
-
AI for Autonomous Defense Drones & Surveillance
The use of autonomous drones in defense is revolutionizing surveillance and reconnaissance missions. By integrating AI, drones can... Learn more -
PolyMath AI Workshop Series: From Data Manifolds to Causal Inference
International Workshop Series on Mathematical Foundations and Advanced Reasoning in AI Join NanoSchool (NSTC) and get certified with... Learn more -
Smart Building Design: Daylight & Ventilation Essentials
A comprehensive 6-week mentor-based e-LMS program designed to equip architects, designers, and sustainability professionals with the science and... Learn more -
Email Ops: Cadence + Templates + Follow-ups
Master Email Ops: Cadence + Templates + Follow-ups and deliver a Template kit + follow-up rules. Designed for... Learn more -
EHS (Environment, Health & Safety) Compliance Professional Program
Professionals in environmental health and safety (EHS) navigate a multifaceted landscape, mastering regulatory compliance, risk assessment, and emergency... Learn more
Learn from Expert Mentors
Connect with industry leaders and academic experts.
What Our Learners Say
Hear from researchers and professionals.
Good
Dr srilatha Ande srilatha.ammu12@gmail.com
AI for Psychological and Behavioral Analysis
Good and Very Informative and learnt new things
K.Lakshmi Surekha
He was well-organized and good presenter
Rim Abdul kader Mousa
He was kind and humble to answer all the questions.
Rajkumar Rengaraj
NanoBioTech Workshop: Integrating Biosensors and Nanotechnology for Advanced Diagnostics
Excellent Presentation and Guidance in AI assisted design of composite materials by the mentor.
RAJKUMAR GUNTI rajkumar.gunti@gmail.com
AI-Assisted Composite Materials Design
All facilities have explained everything nicely.
Veenu Choudhary
Scientific Paper Writing: Tools and AI for Efficient and Effective Research Communication
What You’ll Gain
- Full access to e-LMS
- Publication opportunity
- Self-assessment & final exam
- e-Certificate
Reviews
There are no reviews yet.