## 1 Introduction
Since this decision is made solely on the basis of the experienced engineers' knowledge, different engineers may make different conclusions because of their biased domain expertise. In other words, the same wafer that is considered \"normal\" by one engineer may be deemed \"bad\" by another. More specifically, when a WBM is inspected by an engineer, he/she makes a bad wafer decision by focusing only on the types of test failures that are familiar based on  
Figure 1: Examples of WBMs with different coloring schemes: **(a)** WBM in which bins that passed all EDS tests are white and all the other bins are gray, **(b)** WBM in which bins that passed all EDS tests are white and other bins are randomly assigned a color according to the bin code. his/her experiences. In other words, he/she looks at only a few bin codes (WBM colors) to make a decision rather than considering all the bin codes. Consequently, depending on the WBM colors that the engineer focuses on, the same wafer can be identified as either \"normal\" or \"bad.\" This problem is mainly caused by the limitations of the current WBM coloring scheme. When the gray WBM is used, significant information loss is inevitable since the engineer can only see the dies that failed at least one test; it is impossible to see which dies failed at what specific tests. With the random RGB coloring scheme, the role of color is to indicate unique bin codes; similar colors do not mean that the failed tests they represent are physically or electrically similar. Thus, existing WBM colors are not fully used since engineers use color information only to distinguish one bin code from another. If WBM colors can provide the relationship between two bin codes, i.e., two sets of failed tests, then engineers can better understand the WBM and identify significant patterns for improved decision-making. To address the limitations of the abovementioned WBM coloring schemes, we propose a new, neural network-based WBM coloring scheme called bin-to-vector (Bin2 Vec) 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 Bin2 Vec 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. The main idea behind Bin2 Vec is that if two dies are physically close to each other, then it is highly likely that their EDS results are very similar; they might even pass and fail the same tests together. To realize this idea, Bin2 Vec adopts the word-to-vector (Word2 Vec) structure, which has been successfully used in various natural language processing tasks [8; 9]. We expect that once the Bin2 Vec succeeds in learning the local structure of EDS test results of closely located dies, the resulting RGB codes can not only discriminate one bin code from another but the RGB code can also represent the EDS test result similarity between any pair of bin codes. Moreover, these similarities can be visualized in a two-dimensional space to help engineers better understand different WBM patterns. In addition to the Bin2 Vec, we also propose a convolutional neural network (CNN)-based bad wafer classification model, which has been done manually by experienced engineers. CNNs have shown excellent performance in various areas such as audio processing and image processing tasks, including image classification [10; 11; 12; 13; 14; 15]. The bad wafer classification model takes the WBM as the input image, and determines whether it is normal or bad as the output of the network. To train the CNN model, historical data consisting of WBMs and their corresponding normal/bad labels determined by experienced engineers are used. If the CNN-based bad wafer classification model shows a certain level of accuracy, then WBMs can be automatically classified without the intervention of engineers. The rest of this paper is organized as follows. In Section 2, we briefly review previous studies related to WBM-based bad wafer classification models.