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INEMI Smart Manufacturing Tech Topic Series: Enhancing Yield and Quality with Explainable AI
May 2, 2025 | iNEMIEstimated reading time: 1 minute
In semiconductor manufacturing, the ability to analyze vast amounts of high-dimensional data is critical for ensuring product quality and optimizing wafer yield. Traditional supervised machine learning approaches struggle with data sparsity, imbalance, and the need for extensive labeled datasets, particularly during new product introduction (NPI).
To address these challenges, this webinar introduces an explainable AI framework that integrates deep topological data analysis (DTDA) and self-supervised learning (SSL). DTDA, an unsupervised machine learning method, transforms complex datasets into two-dimensional networks, where nodes represent clusters of similar samples, enabling identification of key features impacting yield and quality and providing actionable insights for corrective measures. SSL complements this by leveraging large amounts of unlabeled data to detect patterns and anomalies without explicit supervision.
Transfer learning further enhances capabilities, enabling rapid adaptation to new datasets while reducing computational overhead. By automating clustering and grouping, it enhances process monitoring, defect identification, and decision-making, ultimately improving yield and reducing costs.
This framework holds potential for broader applications beyond semiconductor manufacturing, especially in industrial settings requiring analysis and extraction of insights from large-scale, high-dimensional datasets.
About the Speaker Janhavi Giri, PhD
Intel Corporation
Dr. Giri is a data scientist with more than a decade of experience in the semiconductor industry. She specializes in applying advanced machine learning techniques, including topological data analysis (TDA) and causal AI, to solve high-dimensional data challenges in semiconductor manufacturing. Her expertise focuses on yield optimization, equipment productivity, root cause analysis, and high-dimensional data mining. Dr. Giri holds advanced degrees in Physics and Applied Mathematics and is passionate about bridging AI innovations with manufacturing to drive smarter, more efficient solutions.
Future of Electronics Series
This webinar is the first in a year-long series on the intersection of smart manufacturing, sustainability and systems integration. Watch the INEMI calendar for additional webinars in this series.
Registration
This webinar is open to industry; advance registration is required.
Tuesday, May 6, 2025
11:00 a.m. – 12:00 p.m. EDT (North America)
5:00-6:00 p.m. CEST (Europe)
Get additional details and register for this webinar
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