Data-Driven Process Analytics for Semiconductor Tools: From Early Drift Detection to Predictive Optimization
| Start Time | End Time |
|---|---|
| 8 Apr 2026, 2:00 PM EDT | 8 Apr 2026, 3:00 PM EDT |
Overview
Data-driven analytics play a pivotal role in semiconductor manufacturing, where engineers must navigate hundreds of interdependent process variables, tight performance windows, and costly experimentation. By harnessing advanced analytics, teams can rapidly uncover process insights, predict outcomes, and optimize parameters, enabling faster and more reliable recipe development.
Join our webinar to learn how data-driven equipment monitoring integrates high frequency sensor data, tool logs, metrology outputs, and historical maintenance records to build a comprehensive view of equipment behavior over time. Come learn how engineers can model these data streams, to identify early signatures of tool drift, component wear, or suboptimal operating conditions long before they impact process results. This allows maintenance teams to schedule interventions proactively, reducing unplanned downtime and extending equipment life.
Highlights
This webinar will cover:
- Gaussian Process Regression (GPR) for process modeling, feature engineering, and identifying key drivers of recipe performance.
- Multi-objective optimization for balancing competing process metrics and pareto solution.
- Anomaly detection and predictive maintenance for monitoring the health of the equipment.
About the Presenter
Garima Sharma
Application Engineer, MathWorks
As an application engineer, Garima consults with customers to help them incorporate their defined needs to create tailor-made solutions to enhance their workflow. Her primary competencies are biomedical signal processing, traditional machine learning, and modern deep learning algorithms. She has experience working with different signal modalities such as audio, image, and oscillation signals. Before joining MathWorks, Garima was pursuing her Doctoral degree in electrical and computer engineering from Toronto Metropolitan University.
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