Predictive System of Semiconductor Failures based on Machine Learning Approach

Mourabit, Yousef El; Habouz, Youssef El; Zougagh, Hicham; Wadiai, Younes

doi:10.14569/ijacsa.2020.0111225

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…Recent prediction methods consider long-range dependencies and heavy tail characteristics in the assessment of RUL [14]. The methods for classification tasks include Hierarchical Clustering (HC) [15], Support Vector Machine (SVM) [16,17], Decision Tree (DT) [18], and Random Forest (RF) [19]. In the field of deep learning, methods such as Convolutional Neural Network (CNN) combined with LSTM have been employed [20].…”

Section: A Backgroundmentioning

confidence: 99%

Fuzzy Few-Shot Learning for Predictive Maintenance and Cost-Benefit Analysis in Semiconductor Manufacturing: A Case Study on Photomask Haze

Huang,

Huang

2024

IEEE Access

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Predictive Maintenance (PdM) plays a key role in production management by extending the lifespan of components and reducing maintenance costs. In the manufacture of semiconductors, the scarcity of data related to haze defects makes it difficult to draw correlations between environmental factors and haze formation. The uncertainty associated with a reliance on indirect evidence (i.e., cumulative time in the environment) has seldom been explored in the literature. Therefore, we developed a PdM framework based on fuzzy few-shot learning to deal with photomask haze in the semiconductor industry. The robustness of the model was evaluated in a three-month pilot study conducted in a wafer foundry. This paper also provides cost-benefit analysis of model implementation. The results demonstrate that the proposed haze photomask detection model outperforms existing models in terms of hit rates and false alarm rates. It also proved effective in lowering labor costs and power consumption, as evidenced by the fact that the number of haze candidates was well below the daily inspection cap, which allowed the decommissioning of one photomask inspection device, reducing the photomask inspection volume by 21%, which is equivalent to an annual labor cost reduction of USD 18,780. These results should help to promote the adoption of predictive maintenance applications, even in situations with small sample sizes.

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Section: A Backgroundmentioning

confidence: 99%

Fuzzy Few-Shot Learning for Predictive Maintenance and Cost-Benefit Analysis in Semiconductor Manufacturing: A Case Study on Photomask Haze

Huang,

Huang

2024

IEEE Access

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“…The high complexity of the semiconductor production system is not compatible with a unified framework able to face all related PdM problems. However, some categorizations are attempted in the literature aimed at defining groups of homogeneous frameworks from the system point of view [ 18 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

“…In practice, each application of the semiconductor industry together with its endowed methodology for PdM can be considered as a case study. PdM has been applied, e.g., to transport of wafers [ 43 , 44 ], prediction of wafer failures [ 27 , 37 , 45 , 46 , 47 , 48 ], ion-beam etching [ 49 , 50 ], supply chain [ 51 ], vibration during normal operation [ 44 ], and pump and abatement equipment [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

Predictive Maintenance of Pins in the ECD Equipment for Cu Deposition in the Semiconductor Industry

Amato,

Antoniadis,

De Feis

et al. 2023

Sensors

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Nowadays, Predictive Maintenance is a mandatory tool to reduce the cost of production in the semiconductor industry. This paper considers as a case study a critical part of the electrochemical deposition system, namely, the four Pins that hold a wafer inside a chamber. The aim of the study is to replace the schedule of replacement of Pins presently based on fixed timing (Preventive Maintenance) with a Hardware/Software system that monitors the conditions of the Pins and signals possible conditions of failure (Predictive Maintenance). The system is composed of optical sensors endowed with an image processing methodology. The prototype built for this study includes one optical camera that simultaneously takes images of the four Pins on a roughly daily basis. Image processing includes a pre-processing phase where images taken by the camera at different times are coregistered and equalized to reduce variations in time due to movements of the system and to different lighting conditions. Then, some indicators are introduced based on statistical arguments that detect outlier conditions of each Pin. Such indicators are pixel-wise to identify small artifacts. Finally, criteria are indicated to distinguish artifacts due to normal operations in the chamber from issues prone to a failure of the Pin. An application (PINapp) with a user friendly interface has been developed that guides industry experts in monitoring the system and alerting in case of potential issues. The system has been validated on a plant at STMicroelctronics in Catania (Italy). The study allowed for understanding the mechanism that gives rise to the rupture of the Pins and to increase the time of replacement of the Pins by a factor at least 2, thus reducing downtime.

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“…These techniques, which extract key features from data and correlate them to possible faults [3] are successfully applied in condition monitoring of e.g. gearbox [4][5][6], rotating machinery [7], motor bearings [8], centrifugal pumps [9,10], hydraulic systems [11], cutting tools [12], grinding mill liners [13], and semiconductor failures [14].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Analysis for Sensor-Based Hydraulic System Condition Monitoring

Alenany¹,

Helmi²,

Nasef³

2021

IJACSA

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Condition monitoring of equipment can be very effective in predicting faults and taking early corrective actions. As hydraulic systems constitute the core of most industrial plants, predictive maintenance of such systems is of vital importance. Due to the availability of huge data collected from industrial plants, machine learning can be used for this purpose. In this work, a hydraulic system condition monitoring (HSCM) is addressed via a public dataset with 17 sensors distributed throughout the system. Using a set of 6 features extracted from sensory data, the random forest classifier was proven, in the literature, to achieve classification rate exceeding 99% for four independent target classes, namely, Cooler, Valve, Pump and Accumulator. In this paper, sensor dependency is examined and experimental results show that a reduced set of important sensors may be sufficient for the addressed classification task. In addition, feature importance as well as implementation issues, i.e. training time and model size on disk, are analyzed. It is found that the training time can be reduced by 25.7% to 36.4% while the size on disk is reduced by 70.3% to 85.5%, using the optimized models, with only important sensors employed, in comparison with the basic model, with full set of sensors, while maintaining classification precision.

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Predictive System of Semiconductor Failures based on Machine Learning Approach

Cited by 8 publications

References 22 publications

Fuzzy Few-Shot Learning for Predictive Maintenance and Cost-Benefit Analysis in Semiconductor Manufacturing: A Case Study on Photomask Haze

Fuzzy Few-Shot Learning for Predictive Maintenance and Cost-Benefit Analysis in Semiconductor Manufacturing: A Case Study on Photomask Haze

Predictive Maintenance of Pins in the ECD Equipment for Cu Deposition in the Semiconductor Industry

Comprehensive Analysis for Sensor-Based Hydraulic System Condition Monitoring

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