Classifying and clustering noisy images using subset learning based on convolutional neural networks

Abstract

A high yield rate is a key factor related to success in the competitive global semiconductor manufacturing business market. Wafer bin maps (WBMs) can be used as one measure of the output quality of a semiconductor manufacturing process. A WBM is the image results from a number of circuit probe (CP) tests on a wafer after the completion of a manufacturing process. The specific defect patterns on WBMs provide crucial information for engineers to trace the causes of defects in the complicated manufacturing process. This study is aimed toward investigating major practical challenges in current WBM analyses. Our approach involved utilizing a small, carefully labeled subset to reduce the labor requirements and human recognition bias related to identification of very noisy images. The first proposed procedure classified the noisy defect patterns by using convolutional neural networks (CNNs) trained with a small subset of labeled WBMs in the early batches. The second proposed procedure provided the proper clusters of noisy defect patterns using the features extracted from the trained CNNs. This procedure made it possible to generate various clusters of WMBs and integrate them in label space. The third procedure separated the new pattern from the existing defect patterns with the help of a supplemental dataset from a similar wafer product. The evaluation of the three proposed procedures was done with simulation data generated from real sets of WBMs, which were added with random noise to maintain confidentiality. The evaluation results demonstrated the practical validity of the proposed procedures.

A Novelty Procedure to Identify Critical Causes of Materials Incompatibility

“…However, the results of these controls still do not show what the main problem is with defects occurring in materials or products [ 17 ]. Therefore, it is necessary to conduct further analyses in this regard to adequately undertake the improvement actions mentioned above [ 18 , 19 ], including those that stabilise the quality of materials and industrial products [ 20 , 21 , 22 ]. Therefore, the objective of the investigation was to develop a procedure to identify critical causes of material incompatibility.…”

“…As part of the aforementioned research, its use supported the process of identifying defects in materials or products for further analysis. Other popular tools are the Pareto-Lorenz analysis, which the authors of the study used, for example [ 20 , 21 , 22 , 28 , 29 , 30 ]. Pareto analysis was used to identify the most frequently repeated incompatibilities between materials or industrial products.…”