Development of cloud-based automatic virtual metrology system for semiconductor industry

Huang, Hsien‐Cheng; Lin, Yu-Chuan; Hung, Min‐Hsiung; Tu, Chia-Chun; Cheng, Fan‐Tien

doi:10.1016/j.rcim.2015.01.005

Cited by 23 publications

(5 citation statements)

References 24 publications

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“…The full cloud VM architecture places every possible component in the cloud, including the VM algorithm. The only part that remains on the machine is the data collection device (Huang et al 2015). Adding VM in a server dramatically increases the server's resistance to software failure thanks to the easy replacement of virtual machines; computational power can also easily be higher.…”

Section: Fab-wide Architecturementioning

confidence: 99%

Virtual metrology as an approach for product quality estimation in Industry 4.0: a systematic review and integrative conceptual framework

Dreyfus

Psarommatis

May

et al. 2021

International Journal of Production Research

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Virtual metrology (VM) involves estimating a product's quality directly from production process data without physically measuring it. This enables the product quality of each unit of production to be monitored in real time, while preserving the process efficiency. Initially developed for the semiconductor industry, VM has recently been examined for use in other industrial fields. VM is enabled by components such as quality estimators and drift detectors. It enhances various industrial applications such as machine control and sampling decision systems. The literature lacks a comprehensive summary and a systematic review of the state of the art in VM. To fill this gap, this study followed a systematic methodology to conduct a complete and structured literature review of VM. This paper presents a detailed analysis of the 199 papers that we identified based on our search and selection criteria. Following our methodological framework, we assigned these papers to categories and highlighted shortcomings. Finally, we discussed VM's use in various industrial fields, underlining its potential for every manufacturing industry. This study and the proposed framework are useful for both practitioners and academicians since they raise crucial theoretical and managerial questions as well as provide suggestions for further research on this critical topic.

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Section: Fab-wide Architecturementioning

confidence: 99%

Virtual metrology as an approach for product quality estimation in Industry 4.0: a systematic review and integrative conceptual framework

Dreyfus

Psarommatis

May

et al. 2021

International Journal of Production Research

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“…Another promising technology is cloud computing, an emerging trend well suitable for scenarios where computational power, accessibility, agility and scalability are necessary. The extension of cloud computing to industry results in the concept of cloud-based manufacturing, a vital element for IoT and cyber-physical systems (Huang et al, 2015). According to Mell et al (2011), cloud computing must present the following key characteristics:…”

Section: Predictive Maintenance Systemsmentioning

confidence: 99%

Implementation of a Predictive Maintenance System using Unsupervised Anomaly Detection

Kremer

Cunha

Silva

2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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This work presents the implementation and architecture of a predictive maintenance system using unsupervised learning, aiming to provide an approach where its benefits are returned to the users faster than traditional system found in the literature. Equipment faults and maintenance affect directly efficiency of industrial plants. Maintenance management can be optimized by modeling and predicting problems. The implementation of a system able to model problems or anomalies and to inform operators and supervisors in case of alarms or notifications is intrinsically connected with the fourth industrial revolution. Although the architecture proposed here can be generalized to many different devices, a MPU6050 for sensing and ESP32 as the middleware were used to implement the proposed concept. Three different methods for anomaly detection were implemented and deployed to Google Cloud, using Compute Engine service. The developed web-server that provides a dashboard to visualize time-series of the sensed physical magnitudes and historical data about all anomalies detected is presented. Therefore, resulting in a platform with intelligence to detect and report problems and abnormalities to employees of industrial plants.

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“…To bridge the gap between direct sensing and indirect sensing, virtual sensing, as a complement to physical sensing, has emerged as a viable, noninvasive, and cost effective method to infer difficult-to-measure or expensive-to-measure parameters in dynamic systems based on computational models [22]. It has been investigated for active noise and vibration control [23], industrial process control [24], building operation optimization [25], lead-through robot programming [26], product quality of semiconductor industry [27], and tool condition monitoring [28,29].…”

Section: Introductionmentioning

confidence: 99%

Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing

Wang

Xie

Zhao

et al. 2017

Robotics and Computer-Integrated Manufacturing

181

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a b s t r a c tPervasiveness of ubiquitous computing advances the manufacturing scheme into a ubiquitous manufacturing era which poses significant challenges on sensing technology and system reliability. To improve manufacturing system reliability, this paper presents a new virtual tool wear sensing technique based on multisensory data fusion and artificial intelligence model for tool condition monitoring. It infers the difficult-to-measure tool wear parameters (e.g. tool wear width) by fusing in-process multisensory data (e.g. force, vibration, etc.) with dimension reduction technique and support vector regression model. Different state-of-the-art dimension reduction techniques including kernel principal component analysis, locally linear embedding, isometric feature mapping, and minimum redundancy maximum relevant method have been investigated for feature fusion in a virtual sensing model, and the kernel principal component analysis performs best in terms of sensing accuracy. The effectiveness of the developed virtual tool wear sensing technique is experimentally validated in a set of machining tool run-to-failure tests on a computer numerical control milling machine. The results show that the estimated tool wear width through virtual sensing is comparable to that measured offline by a microscope instrument in terms of accuracy, moreover, in a more cost-effective manner.

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Development of cloud-based automatic virtual metrology system for semiconductor industry

Cited by 23 publications

References 24 publications

Virtual metrology as an approach for product quality estimation in Industry 4.0: a systematic review and integrative conceptual framework

Virtual metrology as an approach for product quality estimation in Industry 4.0: a systematic review and integrative conceptual framework

Implementation of a Predictive Maintenance System using Unsupervised Anomaly Detection

Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing

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