Next generation advanced process control: Leveraging big data and prediction

Moyne, James; Schulze, Brad; Iskandar, Jimmy; Armacost, M.

doi:10.1109/asmc.2016.7491123

Cited by 9 publications

(8 citation statements)

References 4 publications

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“…Here, top APC specialists from the microelectronics manufacturing user and supplier communities reached consensus on the following points: (1) "[There is a] need for automation of front-end of FD from model building to limits management, but keep in process and equipment expertise"; and (2) a major pain point in today's FDC systems is false and/or missed alarms. This fact was verified in the field; for example, a deployment expert indicated that it often takes up to two weeks to correctly configure univariate FD for a process tool, including collecting data, refining limits, and correlating limits violations to actual events of importance, and even after this process is completed there are often too many false alarms or missed alarms associated with a particular FD model [29].…”

Section: Next Generation Fault Detection and Classification (Ng-fdc)mentioning

confidence: 99%

“…However, because it does not incorporate SME very well, it can result in high numbers of false and missed alarms, lack appropriate prioritization of alarms, etc. [6,29].…”

Section: Next Generation Fault Detection and Classification (Ng-fdc)mentioning

confidence: 99%

“…The big data evolution afforded an opportunity in semiconductor manufacturing to provide an improved FDC capability that addresses these key pain points [6,29,47]. The FD portion of this NG-FDC technology contains two key components.…”

Section: Next Generation Fault Detection and Classification (Ng-fdc)mentioning

confidence: 99%

“…As an example of an analytic, principal component analysis (PCA), often used for MVA FD and EHM, is unsupervised, reactive, MVA, generally static, stateless, and does not formally incorporate SME [28]. With respect to analysis applications, FD in semiconductor fabs today (described further below) is largely unsupervised, reactive, UVA, stateless and statistically based with SME incorporated in FD model development [6,10,12,14,29]. Effective PdM solutions today are supervised, predictive, MVA, and stateless with SME incorporated in fault understanding and variable down-selection.…”

Section: Typical Apc Applications and Analytics Approaches In Today'smentioning

confidence: 99%

“…Figure 4 illustrates the approach to FD that is pervasive in the industry today. The vast majority of effective FD solutions are unsupervised and univariate, analyzing process trace data at the end of each run to detect anomalies [29]. There is a focus on identifying features and applying limits tests to determine if there are alarms.…”

Section: Typical Apc Applications and Analytics Approaches In Today'smentioning

confidence: 99%

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Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing

2017

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Smart manufacturing (SM) is a term generally applied to the improvement in manufacturing operations through integration of systems, linking of physical and cyber capabilities, and taking advantage of information including leveraging the big data evolution. SM adoption has been occurring unevenly across industries, thus there is an opportunity to look to other industries to determine solution and roadmap paths for industries such as biochemistry or biology. The big data evolution affords an opportunity for managing significantly larger amounts of information and acting on it with analytics for improved diagnostics and prognostics. The analytics approaches can be defined in terms of dimensions to understand their requirements and capabilities, and to determine technology gaps. The semiconductor manufacturing industry has been taking advantage of the big data and analytics evolution by improving existing capabilities such as fault detection, and supporting new capabilities such as predictive maintenance. For most of these capabilities: (1) data quality is the most important big data factor in delivering high quality solutions; and (2) incorporating subject matter expertise in analytics is often required for realizing effective on-line manufacturing solutions. In the future, an improved big data environment incorporating smart manufacturing concepts such as digital twin will further enable analytics; however, it is anticipated that the need for incorporating subject matter expertise in solution design will remain.

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Section: Next Generation Fault Detection and Classification (Ng-fdc)mentioning

confidence: 99%

“…However, because it does not incorporate SME very well, it can result in high numbers of false and missed alarms, lack appropriate prioritization of alarms, etc. [6,29].…”

Section: Next Generation Fault Detection and Classification (Ng-fdc)mentioning

confidence: 99%

Section: Next Generation Fault Detection and Classification (Ng-fdc)mentioning

confidence: 99%

Section: Typical Apc Applications and Analytics Approaches In Today'smentioning

confidence: 99%

Section: Typical Apc Applications and Analytics Approaches In Today'smentioning

confidence: 99%

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Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing

2017

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Equipment deterioration modeling and cause diagnosis in semiconductor manufacturing

et al. 2021

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Condition‐based monitoring (CBM) as a new control scheme suggests characterizing the machine condition and triggering the corresponding control actions. CBM includes prognostic and diagnostic modules. In this study, the framework of equipment deterioration modeling and monitoring for batch processes is proposed with two objectives in the semiconductor industry. The first one is to characterize equipment behavior by exploiting the temporal data of batch processes. The second one is to model the deterioration trend with the most related causes. With the best‐fitted mother wavelet, wavelet packet decomposition transforms the temporal data into macro and micro level domains to identify two types of deterioration. The determinant of the correlation matrix of the decomposed signals is calculated as the equipment condition, and the factors that account for the deterioration are identified through a stepwise searching algorithm. A case study shows that the proposed methodology can identify influencing factors and model deterioration.

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Research and Application of Key Technologies of Intelligent Manufacturing Based on 5G Vehicle

Tang

Huo

Zhang

et al. 2023

Lecture Notes in Electrical Engineering

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Next generation advanced process control: Leveraging big data and prediction

Cited by 9 publications

References 4 publications

Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing

Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing

Equipment deterioration modeling and cause diagnosis in semiconductor manufacturing

Research and Application of Key Technologies of Intelligent Manufacturing Based on 5G Vehicle

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