Advanced Process Control (APC) and Real Time Dispatch (RTD) system integration for etch depth control process in 300mm Fab

Agrawal, Gaurav; Loh, Soon Yoong; Shebi, Abemelek B.

doi:10.1109/asmc.2015.7164426

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…It has been argued that if more information from the wafer fabs was used in S/D systems, better decisions could be made in determining production schedules [103], [104], [105]. For instance, the incorporation of process capability in S/D would help align high-value, high-precision products with highcapacity tools to minimize scrap and improve profit.…”

Section: B Case Study: Process-capability Aware Real-time Schedulingmentioning

confidence: 99%

“…It employs a rule-based S/D DT to make appropriate S/D plans (production schedules) and updates these plans in response to events in the fab. In this case study, a product called RTD® 1 is used to provide this rule-based configurable "real-time" S/D decision-making capability for a single process type [104]. A model of the fab layout defines process flow and opportunities for S/D decisions.…”

Section: B Case Study: Process-capability Aware Real-time Schedulingmentioning

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 Case Study: Process-capability Aware Real-time Schedulingmentioning

confidence: 99%

Section: B Case Study: Process-capability Aware Real-time Schedulingmentioning

confidence: 99%

A Requirements Driven Digital Twin Framework: Specification and Opportunities

et al. 2020

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“…Traditional semiconductor manufacturing relied on statistical process controls used to monitor the production process. As the process became more complex and refined, process control with finer precision and accuracy was required and APC was proposed [20,21]. APC enables real-time process monitoring and control using sensors to instantly identify and control the critical process shifts [21].…”

Section: Related Workmentioning

confidence: 99%

“…As the process became more complex and refined, process control with finer precision and accuracy was required and APC was proposed [20,21]. APC enables real-time process monitoring and control using sensors to instantly identify and control the critical process shifts [21]. Sensor-based APC studies such as a study that proposed strict control of CD and phase angle in a photomask dry-etch process using the RF sensor and a study using an in situ plasma monitoring sensor to process diagnosis and endpoint detection in etch process were conducted [22][23][24].…”

Section: Related Workmentioning

confidence: 99%

Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment

et al. 2021

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Semiconductor manufacturing comprises hundreds of consecutive unit processes. A single misprocess could jeopardize the whole manufacturing process. In current manufacturing environments, data monitoring of equipment condition, wafer metrology, and inspection, etc., are used to probe any anomaly during the manufacturing process that could affect the final chip performance and quality. The purpose of investigation is fault detection and classification (FDC). Various methods, such as statistical or data mining methods with machine learning algorithms, have been employed for FDC. In this paper, we propose an artificial immune system (AIS), which is a biologically inspired computing algorithm, for FDC regarding semiconductor equipment. Process shifts caused by parts and modules aging over time are main processes of failure cause. We employ state variable identification (SVID) data, which contain current equipment operating condition, and optical emission spectroscopy (OES) data, which represent plasma process information obtained from faulty process scenario with intentional modification of the gas flow rate in a semiconductor fabrication process. We achieved a modeling prediction accuracy of modeling of 94.69% with selected SVID and OES and an accuracy of 93.68% with OES data alone. To conclude, the possibility of using an AIS in the field of semiconductor process decision making is proposed.

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