Abstract
A wafer bin map (WBM), which is the result of an electrical die-sorting test, provides information on which bins failed what tests, and plays an important role in finding defective wafer patterns in semiconductor manufacturing. Current wafer inspection based on WBM has two problems: good/bad WBM classification is performed by engineers and the bin code coloring scheme does not reflect the relationship between bin codes. To solve these problems, we propose a neural network-based bin coloring method called Bin2Vec to make similar bin codes are represented by similar colors. We also build a convolutional neural network-based WBM classification model to reduce the variations in the decisions made by engineers with different expertise by learning the company-wide historical WBM classification results. Based on a real dataset with a total of 27,701 WBMs, our WBM classification model significantly outperformed benchmarked machine learning models. In addition, the visualization results of the proposed Bin2Vec method makes it easier to discover meaningful WBM patterns compared with the random RGB coloring scheme. We expect the proposed framework to improve both efficiencies by automating the bad wafer classification process and effectiveness by assigning similar bin codes and their corresponding colors on the WBM.
Highlights
The semiconductor market is growing at a rapid pace with the advent of the fourth industrial revolution, characterized by the widespread use of the Internet of Things and the use of artificial intelligence technologies in daily life [1,2]
To address the limitations of the abovementioned wafer bin map (WBM) coloring schemes, we propose a new, neural network-based WBM coloring scheme called bin-to-vector (Bin2Vec) that can preserve the relationship between different bin codes to help engineers better understand the WBM and help identify significant patterns on bad wafers
The Bin2Vec maps a scalar bin code onto a three-dimensional continuous vector in order to assign a unique set of RGB values to the bin code
Summary
The semiconductor market is growing at a rapid pace with the advent of the fourth industrial revolution, characterized by the widespread use of the Internet of Things and the use of artificial intelligence technologies in daily life [1,2]. To address the limitations of the abovementioned WBM coloring schemes, we propose a new, neural network-based WBM coloring scheme called bin-to-vector (Bin2Vec) that can preserve the relationship between different bin codes to help engineers better understand the WBM and help identify significant patterns on bad wafers. We expect that once the Bin2Vec succeeds in learning the local structure of EDS test results of closely located dies, the resulting RGB codes can discriminate one bin code from another but the RGB code can represent the EDS test result similarity between any pair of bin codes These similarities can be visualized in a two-dimensional space to help engineers better understand different WBM patterns.
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