The semiconductor manufacturing process is very complex, and it is the most important part of the semiconductor industry. In order to test whether or not wafers are functioning normally, a pass/fail test is conducted; however, time and cost needed for this testing increase as the number of chips increases. To address this, a machine learning technique is adopted and a high-performance classifier is needed to determine whether a pass/fail test is accurate or not. In this paper, a deep belief network (DBN)-based multi-classifier is proposed for fault detection prediction in the semiconductor manufacturing process. The proposed method consists of two phases: The first phase is a data pre-processing phase in which features required for semiconductor data sets are extracted and the imbalance problem is solved. The second phase is to configure the multi-DBN using selected features. A DBN classifier is created for each feature and, finally, fault detection prediction is performed. The proposed method showed excellent performance and can be used in the semiconductor manufacturing process efficiently.

中文翻译：

---

在半导体制造过程中使用基于深度信念网络的多分类器进行故障检测预测

半导体制造工艺非常复杂，是半导体工业中最重要的部分。为了测试晶圆是否正常工作，进行通过/失败测试；然而，随着芯片数量的增加，这种测试所需的时间和成本也会增加。为了解决这个问题，采用机器学习技术，需要高性能分类器来确定通过/失败测试是否准确。在本文中，提出了一种基于深度信念网络（DBN）的多分类器，用于半导体制造过程中的故障检测预测。所提出的方法包括两个阶段：第一阶段是数据预处理阶段，其中提取半导体数据集所需的特征并解决不平衡问题。第二阶段是使用选定的功能配置多 DBN。为每个特征创建一个 DBN 分类器，最后进行故障检测预测。所提出的方法表现出优异的性能，可以有效地用于半导体制造过程。