A conceptual model to enable prescriptive maintenance for etching equipment in semiconductor manufacturing

Biebl, Fabian; Grepl, R.; Jalali, Anahid; Ansari, Fazel; Haslhofer, Bernhard; Boer, P. de; Sihn, Wilfried

doi:10.1016/j.procir.2020.05.012

Cited by 19 publications

(15 citation statements)

References 6 publications

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“…The description of the conceptual model of the prescriptive maintenance strategy for the semiconductor production process was presented by Biebl et al [2]. The core of the decision-making system is a 3-layer Bayesian network, trained to predict the causes of failures and generate recommendations regarding the possibility of potential problems.…”

Section: Implementations Of Prescriptive Maintenance Strategymentioning

confidence: 99%

Analysis of Neural Network Structure for Implementation of the Prescriptive Maintenance Strategy

FILO¹

2022

Terotechnology XII

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Abstract. This paper provides an initial analysis of neural network implementation possibilities in practical implementations of the prescriptive maintenance strategy. The main issues covered are the preparation and processing of input data, the choice of artificial neural network architecture and the models of neurons used in each layer. The methods of categorisation and normalisation within each distinguished category were proposed in input data. Based on the normalisation results, it was suggested to use specific neuron activation functions. As part of the network structure, the applied solutions were analysed, including the number of neuron layers used and the number of neurons in each layer. In further work, the proposed structures of neural networks may undergo a process of supervised or partially supervised training to verify the accuracy and confidence level of the results they generate.

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Section: Implementations Of Prescriptive Maintenance Strategymentioning

confidence: 99%

Analysis of Neural Network Structure for Implementation of the Prescriptive Maintenance Strategy

FILO¹

2022

Terotechnology XII

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“…The level of complexity of every step is frequently equated to that of a medium-sized industrial unit, particularly in such areas such as logistics, planning, control, and data volume, among other steps. Consequently, growing requirements and pressure to perform with a high plant productivity pose a difficult challenge for companies operating in semiconductor manufacturing [1].…”

Section: Of 38mentioning

confidence: 99%

“…The last few decades have seen the birth of a great diversity of products and services associated with electrical and electronic equipment, and witnessed the presence of electronic and electrical equipment in a large number of products and services, which are subject to constant change [1]. During the last few years, since semiconductor manufacturing processes have gradually diminished in size, the number of transistors that can be fabricated on a sole silicon wafer can amount to a billion units [2].…”

Section: Introductionmentioning

confidence: 99%

A Review of Data Mining Applications in Semiconductor Manufacturing

Espadinha-Cruz

Godina

Rodrigues³

2021

Processes

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For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.

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“…The resulting SMP addresses five joint decisions: selection of components to maintain, selection of maintenance levels performed on the selected components, identification of breaks where maintenance tasks are performed, repairpersons selection, and maintenance tasks assignment to selected repairpersons. In the [6] present paper, we address these challenges by proposing a conceptual model to enable prescriptive maintenance in semiconductor manufacturing. Different Machine Learning Algorithms are used to predict Algorithm for optimizing the parameters of the maintenance process according to the state with a constant periodicity of control over the criterion of minimum unit cost of operation time-to-failure intervals for unplanned downtimes.…”

Section: Introductionmentioning

confidence: 99%

Algorithm for optimizing the parameters of the maintenance process according to the state with a constant periodicity of control over the criterion of minimum unit cost of operation

2020

IJATCSE

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The problem of extending the service life of complex radio-electronic products and systems remains relevant in our time. Life extension is based on the effective operation of maintenance and repair systems. Each of these systems has its own strategies with corresponding parameters, and these parameters should be different for different products. The criteria that determine the parameters of these systems are often the criteria for minimizing the unit cost of operation and increasing the coefficient of technical utilization. The choice of criterion is determined by the nature of the tasks that the product (system) solves. Also, the parameters of the reliability of the product are significantly influenced by the parameters of the reliability of its component parts, as well as the correct design decisions.The article solves the problem of developing an algorithm for optimizing the parameters of the maintenance process of a radio-electronic product (system) using a maintenance strategy based on a condition with a constant monitoring frequency. The algorithm was developed for the criterion of minimizing the unit operating cost of an object, which is determined for a given period of operation, while ensuring a given requirement for the level of reliability of the object.

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A conceptual model to enable prescriptive maintenance for etching equipment in semiconductor manufacturing

Cited by 19 publications

References 6 publications

Analysis of Neural Network Structure for Implementation of the Prescriptive Maintenance Strategy

Analysis of Neural Network Structure for Implementation of the Prescriptive Maintenance Strategy

A Review of Data Mining Applications in Semiconductor Manufacturing

Algorithm for optimizing the parameters of the maintenance process according to the state with a constant periodicity of control over the criterion of minimum unit cost of operation

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