Radial Basis Function Neural Networks for LED Wafer Defect Inspection

Chang, Chuan‐Yu; Chang, Yung-Chi; Li, Chun-Hsi; Jeng, MuDer

doi:10.1109/icicic.2007.471

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Su et al [13] used three kinds of neural networks, which are backpropagation (BPN), radial basis function (RBFN), and learning vector quantization (LVQ) networks, in post-sawing semiconductor wafer detection. Chang et al [14] proposed an inspection system, which automatically recognizes the defective patterns. Their system used the Radial basis function neural network (RBFN) that successfully identified the defective dies on the images of LED wafers.…”

Section: Reviewmentioning

confidence: 99%

Automatic Reclaimed Wafer Classification Using Deep Learning Neural Networks

2020

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Silicon wafer is the most crucial material in the semiconductor manufacturing industry. Owing to limited resources, the reclamation of monitor and dummy wafers for reuse can dramatically lower the cost, and become a competitive edge in this industry. However, defects such as void, scratches, particles, and contamination are found on the surfaces of the reclaimed wafers. Most of the reclaimed wafers with the asymmetric distribution of the defects, known as the “good (G)” reclaimed wafers, can be re-polished if their defects are not irreversible and if their thicknesses are sufficient for re-polishing. Currently, the “no good (NG)” reclaimed wafers must be first screened by experienced human inspectors to determine their re-usability through defect mapping. This screening task is tedious, time-consuming, and unreliable. This study presents a deep-learning-based reclaimed wafers defect classification approach. Three neural networks, multilayer perceptron (MLP), convolutional neural network (CNN) and Residual Network (ResNet), are adopted and compared for classification. These networks analyze the pattern of defect mapping and determine not only the reclaimed wafers are suitable for re-polishing but also where the defect categories belong. The open source TensorFlow library was used to train the MLP, CNN, and ResNet networks using collected wafer images as input data. Based on the experimental results, we found that the system applying CNN networks with a proper design of kernels and structures gave fast and superior performance in identifying defective wafers owing to its deep learning capability, and the ResNet averagely exhibited excellent accuracy, while the large-scale MLP networks also acquired good results with proper network structures.

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Section: Reviewmentioning

confidence: 99%

Automatic Reclaimed Wafer Classification Using Deep Learning Neural Networks

2020

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“…This method can be effective to reduce warning errors caused by cluster defect [22]. Chang et al developed an automatic inspection system, which recognizes defective patterns automatically [21]. The Radial Basis Function (RBF) neural network was adopted for inspection processing.…”

Section: Reclaim Wafer Defect Classification Using Backpropagation Nementioning

confidence: 99%

Reclaim Wafer Defect Classification Using Backpropagation Neural Networks

2016

Eminent Association of Pioneers (EAP) August 22-24, 2016 Kuala Lumpur (Malaysia)

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Silicon wafer is a part of main cost of material in semiconductor manufacturing. Reclaim wafers have been extensively used in the semiconductor industry for many years. Reducing the usage of prime wafers and using reclaim wafers is one of the most cost effective solutions for a manufacturing plant. If any defects such as void, scratches, particles, and contamination found on the surface of reclaim wafers, these wafers will be classified as defective ones (NG). Due to previous study, we found reclaim wafers can be re-polished if its defect is not fatal and its thickness is enough for a repolish. Currently, those NG reclaim wafers must be screened by experienced human inspectors to determine re-usability through its defective mapping. This screening task is tedious, costly, and unreliable. This study proposed an artificial intelligent system to construct an automated reclaim wafer defect classification system, in which back-propagation (BPN) were used to analyze the pattern of defective mapping, and determined whether reclaim wafers can be re-polished or not. Experiments showed the BPN gave good performance in identifying NG and Good wafers. The proposed method was successfully increase the reusability of RM, and currently serves as important module in the MIS system of case-study company.

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Radial Basis Function Neural Networks for LED Wafer Defect Inspection

Cited by 2 publications

References 6 publications

Automatic Reclaimed Wafer Classification Using Deep Learning Neural Networks

Automatic Reclaimed Wafer Classification Using Deep Learning Neural Networks

Reclaim Wafer Defect Classification Using Backpropagation Neural Networks

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