Big Data Capabilities Applied to Semiconductor Manufacturing Advanced Process Control

Moyne, James; Samantaray, Jamini; Armacost, M.

doi:10.1109/tsm.2016.2574130

Cited by 71 publications

(40 citation statements)

References 12 publications

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“…In addressing these challenges a family of factory operation analytics called advanced process control (APC) quickly came to the forefront in the 1990s as a tool for competitive advantage and then as a requirement in the early 2000s (see Figure 1) [6,[11][12][13][14][15]. Over the first decade of the 2000s APC capabilities such as on-line equipment and process fault detection (FD) and classification (FDC), and process control (referred to as "run-to-run" or "R2R" control) became pervasive in all front-end semiconductor manufacturing facilities.…”

Section: Semiconductor Manufacturing and The Emergence Of On-line Anamentioning

confidence: 99%

“…As noted earlier data quality issues ranging from accuracy to availability to context richness continue to exist. While data quality continues to improve and is arguably better than in many other industries, due to factors such as stringent processing requirements and adherence to standards, data still cannot be blindly trusted in analytical solutions [6].…”

Section: Yield Predictionmentioning

confidence: 99%

“…One perspective of defining a taxonomy for organizing analytics is described in [25]. Figure 3 provides a breakdown of dimensions relevant to analytics in semiconductor manufacturing, with the dimension descriptions as follows [6]:…”

Section: Dimensions Of Analytics Capabilitiesmentioning

confidence: 99%

“…SM leverages a corresponding trend in manufacturing of the advances in volume, velocity, and variety of data, often referred to as "big data," and applies "big data analytics" to improve existing analysis capabilities and provide new capabilities such as predictive analytics [6]. These capabilities are emerging in all industries, but at different speeds probably due to factors such as installed base, culture, supplier base, and need.…”

Section: Introductionmentioning

confidence: 99%

“…These capabilities are emerging in all industries, but at different speeds probably due to factors such as installed base, culture, supplier base, and need. Thus, the potential exists to look across industries to understand what can be used to improve the capabilities in a particular industry such as biochemistry and biology [6][7][8].…”

Section: Introductionmentioning

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: Semiconductor Manufacturing and The Emergence Of On-line Anamentioning

confidence: 99%

Section: Yield Predictionmentioning

confidence: 99%

Section: Dimensions Of Analytics Capabilitiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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Artificial Intelligence in Manufacturing

Chen

Wang

2022

Artificial Intelligence and Lean Manufacturing

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Big Data Capabilities Applied to Semiconductor Manufacturing Advanced Process Control

Cited by 71 publications

References 12 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

Artificial Intelligence in Manufacturing

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