Convolutional Neural Network for Wafer Surface Defect Classification and the Detection of Unknown Defect Class

Cheon, Sejune; Lee, Hankang; Kim, Chang Ouk; Lee, Seok Hyung

doi:10.1109/tsm.2019.2902657

Cited by 225 publications

(93 citation statements)

References 13 publications

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“…Content may change prior to final publication. [118] Overall quality inspection of wafer [119] IC wafer contamination [120] Micropipes defects [121]- [123] Chip-out, bridging, metal lifting, glassiviation and peel off [124] Wafer topside scratch, foreign material, ink residue, pad damage, passivation/metal damage, ink smeary, and passivation covering [125] Pinhole defects [126] Protrusion, dent, flat and bumpy defects [127] Hole, Protruding and flat patterns [128] Particles, contamination and scratches [129] Defects between line edges [130] Hole, flaw and scratch defects [131] Alignment, probe marks and bump defects for in-tray semiconductor chip [132] Spots, scratches, and bruises [133] Bond pad discoloration [54] Die edge, die street and determination of chipping size and shape [134] Spot, rock-shaped particle, ring-shaped particle, misalignment and scratch [135] Defects are classified as small, medium and large overall functionality of the circuit will be affected. Excess solder joint can cause bridging with other PCB solder joints which can lead to a short circuit.…”

Section: Pcb Defectsmentioning

confidence: 99%

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

2020

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Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, microelectronics , and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.

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Section: Pcb Defectsmentioning

confidence: 99%

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

2020

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“…With the large amount of data collected during the semiconductor manufacturing process, numerous studies attempted to improve the manufacturing process by utilizing the data for predictive modeling [21], [22]. These attempts have been successful in automating manual operations [7], [23], [24], estimating physical inspections [25]- [27], and extracting valuable insights [28]- [30] by learning from data.…”

Section: Related Work a Wafer Map Pattern Classificationmentioning

confidence: 99%

Rotation-Invariant Wafer Map Pattern Classification With Convolutional Neural Networks

Kang

2020

IEEE Access

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The enhancement of production yield is a continuous challenge in semiconductor manufacturing. Analyzing the spatial defect patterns of previously processed wafers is a key step in identifying the root causes of yield degradation. Predictive modeling approaches have been successful in automated wafer map pattern classification. The classification performance depends significantly on the quantity and diversity of data that can be acquired, which are often limited in practice. In this study, we demonstrate that rotation-based data augmentation can effectively improve wafer map pattern classification when training data are scarce. As rotation is a label-preserving transformation for wafer maps, we construct a convolutional neural network with rotation-augmented training data to render the classification invariant with respect to rotation. This enables us to provide consistent predictions for rotational variations of new wafer maps, thereby achieving higher classification performance. The effectiveness of our method is verified based on real-word data from a semiconductor manufacturer.

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“…Existing studies have utilized CNN to detect defect pattern on wafers . Despite the outstanding performance of these CNN‐based methods, they still suffer from various limitations.…”

Section: Introductionmentioning

confidence: 99%

Defect pattern recognition on wafers using convolutional neural networks

Wang

Chen

2020

Quality & Reliability Eng

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In semiconductor manufacturing, wafer testing is performed to ensure the performance of each product after wafer fabrication. The wafer map is used to visualize the color-coded wafer test results based on the locations. The defects on the wafer map may be randomly distributed or form clustered patterns. The various clustered defect patterns are usually caused by assignable faults. The identification of the patterns is thus important to provide valuable hints for the root causes diagnosis. Solving the problems helps improve the manufacturing processes and reduce costs. In this study, we present a novel convolutional neural network (CNN)-based method to automatically recognize the defect pattern on wafer maps. Our method uses polar mapping before the training of CNN to transform the circular wafer map into a matrix, which can be processed within CNN architecture. This procedure also reduces the input size and solves variations in wafer sizes and die sizes. To eliminate the effects of rotation, we apply data augmentation in the training of CNN. Experiments using the real-world dataset prove the effectiveness and superiority of our method.

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Convolutional Neural Network for Wafer Surface Defect Classification and the Detection of Unknown Defect Class

Cited by 225 publications

References 13 publications

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Rotation-Invariant Wafer Map Pattern Classification With Convolutional Neural Networks

Defect pattern recognition on wafers using convolutional neural networks

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