Maintenance of virtual metrology models

Iskandar, Jimmy; Moyne, James

doi:10.1109/asmc.2016.7491083

Cited by 9 publications

(8 citation statements)

References 10 publications

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“…A third insight is that effective DT implementation must include a mechanism to maintain a level of intelligence in the solutions so that the desired benefit can continue to be provided. This usually means that a method of DT maintenance must be part of the solution, and this method must support an update of the DT intelligence to understand and adapt to changes in the application environment [46], [47].…”

Section: B Dt Uses Today and Current Trendsmentioning

confidence: 99%

“…The updates could be implemented online (e.g., with model tuning) or might require the DT to be taken off-line (e.g., for model rebuilding). DT model maintenance is an often underappreciated and weak area in the DT lifecycle [46], [30], [21], [56], [57]. Table 3 for further description of steps and transitions.…”

Section: The Dt Lifecyclementioning

confidence: 99%

“…This also results in improved scalability of the DT solution. While the re-usability concept can quickly become quite complex with the degree of reusability definable, techniques such as non-threaded MBPC and analytical clustering can be leveraged to determine if model development efforts in different context environments can be leveraged [70], [71], [10], [46].…”

Section: Dt Framework Requirements Derived From Dt Applications Inmentioning

confidence: 99%

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A Requirements Driven Digital Twin Framework: Specification and Opportunities

et al. 2020

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Among the tenets of Smart Manufacturing (SM) or Industry 4.0 (I4.0), digital twin (DT), which represents the capabilities of virtual representations of components and systems, has been cited as the biggest technology trend disrupting engineering and design today. DTs have been in use for years in areas such as model-based process control and predictive maintenance, however moving forward a framework is needed that will support the expected pervasiveness of DT technology in the evolution of SM or I4.0. A set of requirements for a DT framework has been derived from analysis of DT definitions, DTs in use today, expected DT applications in the near future, and longer-term DT trends and the DT vision in SM. These requirements include elements of re-usability, interoperability, interchangeability, maintainability, extensibility, and autonomy across the entire DT lifecycle. A baseline framework for DT technology has been developed that addresses many aspects of these requirements and enables the addressing of the requirements more fully through additional specification. The baseline framework includes a definition of a DT and an object-oriented (O-O) architecture for DTs that defines generalization, aggregation and instantiation of DT classes. Case studies using and extending the baseline framework illustrate its advantages in supporting DT solutions and trends in SM.

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Section: B Dt Uses Today and Current Trendsmentioning

confidence: 99%

Section: The Dt Lifecyclementioning

confidence: 99%

Section: Dt Framework Requirements Derived From Dt Applications Inmentioning

confidence: 99%

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A Requirements Driven Digital Twin Framework: Specification and Opportunities

et al. 2020

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“…In practice, it is not possible to capture the impact of all process drifts or maintenance related shifts in VM models and, therefore, model maintenance strategies such as those discussed in [30] are vital for successful long term deployment.…”

Section: Vm-enabled R2r Control Using Gprmentioning

confidence: 99%

Gaussian Process Regression for Virtual Metrology-Enabled Run-to-Run Control in Semiconductor Manufacturing

Wan

McLoone

2018

IEEE Trans. Semicond. Manufact.

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“…Indeed, these same techniques can be leveraged for other predictive capabilities such as After models and limits have been set up for a system, they must be managed in an environment that is oftentimes very dynamic, with numerous process and product disturbances and context changes (e.g., product changes). Model and limits management remains a challenge, but is often accomplished with a variety of tools for model pre-treatment to address context, model continuous update to support process dynamics, and model and limits update triggering and rebuilding or retargeting [51].…”

Section: Predictive Maintenance (Pdm)mentioning

confidence: 99%

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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Maintenance of virtual metrology models

Cited by 9 publications

References 10 publications

A Requirements Driven Digital Twin Framework: Specification and Opportunities

A Requirements Driven Digital Twin Framework: Specification and Opportunities

Gaussian Process Regression for Virtual Metrology-Enabled Run-to-Run Control in Semiconductor Manufacturing

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

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