There have been huge advancements in the field of Artificial Intelligence in the last decade. While this has been fueled by better compute, more data, and cheaper storage, the real success which has revolutionized AI applications is a combination of deep learning and transfer learning. This session will give a gentle introduction to deep learning, deep transfer learning, cover its scope, advantages, applications, and limitations. We will then do a deep-dive into three industry-focused use-cases pertaining to Semiconductors and Manufacturing where we have leveraged a combination of deep transfer learning and traditional computer vision to solve these challenging, yet interesting problems at nanoscale.
Chip and equipment manufacturing is a tough task given the strict adherence to quality standards and processes like six-sigma control checks. In this session, we will be looking at ways in which we leveraged a combination of traditional image processing and computer vision techniques and coupled it with the power of deep transfer learning and deep learning methodologies including classification, clustering, object detection, and text recognition. Following is a brief on the three use-cases we will be covering in the session:
Automated Defect Classification at Nanoscale: EUV masks are critical towards the manufacturing of integrated circuit designs. Defects in these masks can be highly costly as they end up degrading the quality of manufactured wafers. Leveraging a combination of traditional image processing, classification, and object recognition coupled with deep transfer learning, we will look at how to detect defects at nanoscale from low-resolution scanner images and classify them using a hierarchical multi-level classifier.
Automated Defect Clustering at Nanoscale: Masks are critical to ensure the right patterns are formed on wafer during the manufacturing processed. However, there are a wide variety of defects that could occur in these masks. These defects need to be detected and analyzed by process engineers. The classification works well when you know what defects to look at, but how do you deal with unknowns? This is where we leveraged a combination of deep transfer learning coupled with unsupervised learning to build an automated defect clustering solution
Generic Optical Character Recognition for Inventory Tracking: Typically there is a wide variety of inventory equipment that needs to be tracked based on artifacts like barcodes, engraved text, printed text, and so on. Usually, these artifacts vary across equipment in the form of various formats, shapes, sizes, orientations, and textures. We will talk about a generic optical character recognition system that leverages traditional image processing techniques as well as deep learning-based object detection models to extract text from equipment with varying formats.
This is going to be a practitioner-focused talk so we will be looking at how deep transfer learning coupled with traditional computer vision has been leveraged for solving the above use-cases and also discuss model architectures, tools, and techniques that were used in the process.
Tutorial Overview and Author Bio
Deep Transfer Learning for Computer Vision: Real-World Applications at Nanoscale
Data Science Lead | Google Developer Expert - ML | Applied Materials | Google
Deputy Director - Data Science | Applied Materials