A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes

Lee, Ki‐Bum; Cheon, Sejune; Kim, Chang Ouk

doi:10.1109/tsm.2017.2676245

Cited by 405 publications

(152 citation statements)

References 8 publications

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“…A deep structure machine learning approach is also proposed in [24] to identify and classify both single-defect and mixed-defect patterns by incorporating an information gainbased splitter as well as deep-structured machine learning. A fault detection and classification CNN model with automatic feature extraction and fault diagnosis functions has been proposed in [25] to model the structure of the multivariate sensor signals from a semiconductor manufacturing process.…”

Section: Related Workmentioning

confidence: 99%

Fault Diagnosis in Microelectronics Attachment Via Deep Learning Analysis of 3-D Laser Scans

Dimitriou¹,

Leontaris²,

Vafeiadis³

et al. 2020

IEEE Trans. Ind. Electron.

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A common source of defects in manufacturing miniature Printed Circuits Boards (PCB) is the attachment of silicon die or other wire bondable components on a Liquid Crystal Polymer (LCP) substrate. Typically, a conductive glue is dispensed prior to attachment with defects caused either by insufficient or excessive glue. The current practice in electronics industry is to examine the deposited glue by a human operator a process that is both time consuming and inefficient especially in preproduction runs where the error rate is high. In this paper we propose a system that automates fault diagnosis by accurately estimating the volume of glue deposits before and even after die attachment. Network (3DCNN). RNet outperforms other deep architectures and is able to estimate the volume either directly from the point cloud of a glue deposit or more interestingly after die attachment when only a small part of glue is visible around each die. The entire methodology is evaluated under operational conditions where the proposed system achieves accurate results without delaying the manufacturing process. To this end a modular scanning system is deployed that produces high resolution point clouds whereas the actual estimation of glue volume is performed by (R)egression-Net (RNet), a 3D Convolutional NeuralIndex Terms-3DCNN, deep learning, PCB, smart manufacturing.

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Section: Related Workmentioning

confidence: 99%

Fault Diagnosis in Microelectronics Attachment Via Deep Learning Analysis of 3-D Laser Scans

Dimitriou¹,

Leontaris²,

Vafeiadis³

et al. 2020

IEEE Trans. Ind. Electron.

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“…Several previous studies have conducted the anomaly detection via classification approach with various methods [7,8,9,10,11]. However, there are some problem for using classification method such as difficulty of collecting diverse abnormal cases and labeling cost from collected data to specific category.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection in Particulate Matter Sensor Using Hypothesis Pruning Generative Adversarial Network

Park

Kim

2020

Preprint

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World Health Organization (WHO) provides the guideline for managing the Particulate Matter (PM) level because when the PM level is higher, it threats the human health. For managing PM level, the procedure for measuring PM value is needed firstly. We use Tapered Element Oscillating Microbalance (TEOM)-based PM measuring sensors because it shows higher cost-effectiveness than Beta Attenuation Monitor (BAM)-based sensor. However, TEOM-based sensor has higher probability of malfunctioning than BAM-based sensor. In this paper, we call the overall malfunction as an anomaly, and we aim to detect anomalies for the maintenance of PM measuring sensors. We propose a novel architecture for solving the above aim that named as Hypothesis Pruning Generative Adversarial Network (HP-GAN). We experimentally compare the several anomaly detection architectures to certify ours performing better.

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“…The direct utilization of a deep CNN for a 1-D signal processing application naturally needs a proper 1D to 2D conversion. Recently, researchers have tried to use deep CNNs for fault diagnosis of bearings [36][37][38][39][40][41][42][43][44]. For this purpose, different conversion techniques have been utilized to represent the 1D vibration signals in 2D.…”

Section: Introductionmentioning

confidence: 99%

1-D Convolutional Neural Networks for Signal Processing Applications

Kıranyaz

İnce

Abdeljaber

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

274

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During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and subsampling layers. Deep 2D CNNs with many hidden layers and millions of parameters have the ability to learn complex objects and patterns providing that they can be trained on a massive size visual database with ground-truth labels. With a proper training, this unique ability makes them the primary tool for various engineering applications for 2D signals such as images and video frames. Yet, this may not be a viable option in numerous applications over 1D signals especially when the training data is scarce or application-specific. To address this issue, 1D CNNs have recently been proposed and immediately achieved the state-of-the-art performance levels in several applications such as personalized biomedical data classification and early diagnosis, structural health monitoring, anomaly detection and identification in power electronics and motor-fault detection. Another major advantage is that a real-time and low-cost hardware implementation is feasible due to the simple and compact configuration of 1D CNNs that perform only 1D convolutions (scalar multiplications and additions). This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field. Their state-of-the-art performance is highlighted concluding with their unique properties. The benchmark datasets and the principal 1D CNN software used in those applications are also publically shared in a dedicated website.

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A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes

Cited by 405 publications

References 8 publications

Fault Diagnosis in Microelectronics Attachment Via Deep Learning Analysis of 3-D Laser Scans

Fault Diagnosis in Microelectronics Attachment Via Deep Learning Analysis of 3-D Laser Scans

Anomaly Detection in Particulate Matter Sensor Using Hypothesis Pruning Generative Adversarial Network

1-D Convolutional Neural Networks for Signal Processing Applications

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