Hello trend,
This is Satya Mallick from LearnOpenCV.com.
Today's post is the third prize winner of the recently concluded LearnOpenCV Blog Olympics.
Modern integrated circuits pack more than 100 million transistors in each square millimeter!
When the dimensions are so incredibly tiny, there is little margin for error. Without extensive inspection and metrology (measurement) runs, the process technology would have an unacceptable yield (i.e., most of the chips produced will be unusable.).
Regular readers of this newsletter know that Electron Microscopes are used when we need to resolve fine microscopic details because the wavelength of electrons is much shorter than the wavelength of photons. Image taken using Scanning Electron Microscopes (SEM) are very popular for defect verification and classification in the semiconductor industry.
Let's dive into today's post to understand a specific inspection problem in this domain and how the authors solved it using Deep Learning in the real world. You will also learn when the problems get tricky, it is a good idea to use an ensemble (collection) of many models to obtain higher accuracy.
While the authors have shared all the details of their implementation, they cannot share the actual code due to IP and confidentiality reasons.
As you can see in the post, the problems in this domain are cast as a classification, detection, or segmentation problem.
You'd be surprised how many applications you can develop in computer vision by having a good knowledge of just these three categories of problems.
Second, you will notice that when we have enough data, Deep Learning is the way to solve classification, detection, and segmentation problems.
Our course Deep Learning with PyTorch covers all three categories, and builds a strong foundation by teaching various concepts using practical examples. The course is designed to help a motivated beginner develop expert level skills in deep learning in about four months.
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Cheers!
Satya
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