A Neural-Network Approach To Recognize Defect Spatial Pattern In Semiconductor Fabrication
نویسندگان
چکیده
Yield enhancement in semiconductor fabrication is important. Even though IC yield loss may be attributed to many problems, the existence of defects on the wafer is one of the main causes. When the defects on the wafer form spatial patterns, it is usually a clue for the identification of equipment problems or process variations. This research intends to develop an intelligent system, which will recognize defect spatial patterns to aid in the diagnosis of failure causes. The neural-network architecture named adaptive resonance theory network 1 (ART1) was adopted for this purpose. Actual data obtained from a semiconductor manufacturing company in Taiwan were used in experiments with the proposed system. Comparison between ART1 and another unsupervised neural network, self-organizing map (SOM), was also conducted. The results show that ART1 architecture can recognize the similar defect spatial patterns more easily and correctly.
منابع مشابه
A Spatial Point Pattern Analysis to Recognize Fail Bit Patterns in Semiconductor Manufacturing
The yield management system is very important to produce high-quality semiconductor chips in the semiconductor manufacturing process. In order to improve quality of semiconductors, various tests are conducted in the post fabrication (FAB) process. During the test process, large amount of data are collected and the data includes a lot of information about defect. In general, the defect on the wa...
متن کاملHybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing
Semiconductor manufacturing involves lengthy and complex processes, and hence is capital intensive. Companies compete with each other by continuously employing new technologies, increasing yield, and reducing costs. Yield improvement is increasingly important as advanced fabrication technologies are complicated and interrelated. In particular, wafer bin maps (WBM) that present specific failure ...
متن کاملBayesian spatial defect pattern recognition in semiconductor fabrication using support vector clustering
Defects generated during integrated circuit (IC) fabrication processes are classified into global defects and local defects according to their generation causes. Spatial patterns of locally clustered defects are likely to contain the information related to their defect generation mechanisms. In this paper, we propose a model-based clustering for spatial patterns of local defects to reflect real...
متن کاملPattern Recognition in Control Chart Using Neural Network based on a New Statistical Feature
Today for the expedition of the identification and timely correction of process deviations, it is necessary to use advanced techniques to minimize the costs of production of defective products. In this way control charts as one of the important tools for the statistical process control in combination with modern tools such as artificial neural networks have been used. The artificial neural netw...
متن کاملA New Statistical Approach for Recognizing and Classifying Patterns of Control Charts (RESEARCH NOTE)
Control chart pattern (CCP) recognition techniques are widely used to identify the potential process problems in modern industries. Recently, artificial neural network (ANN) –based techniques are very popular to recognize CCPs. However, finding the suitable architecture of an ANN-based CCP recognizer and its training process are time consuming and tedious. In addition, because of the black box ...
متن کامل