Quality Prediction in Semiconductor Manufacturing processes Using Multilayer Perceptron Feedforward Artificial Neural Network *
| Autor: | Michel Combal, Bouchra Ananou, Jacques Pinaton, Mohammed Al-kharaz, Mustapha Ouladsine |
|---|---|
| Přispěvatelé: | Pronostic-Diagnostic Et CommAnde : Santé et Energie (PECASE), Laboratoire d'Informatique et Systèmes (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), STMicroelectronics |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0303 health sciences
0209 industrial biotechnology Artificial neural network Semiconductor device fabrication Computer science media_common.quotation_subject Feed forward Data transformation (statistics) Context (language use) 02 engineering and technology computer.software_genre 03 medical and health sciences 020901 industrial engineering & automation Multilayer perceptron [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering Quality (business) Data mining Throughput (business) computer ComputingMilieux_MISCELLANEOUS 030304 developmental biology media_common |
| Zdroj: | 2019 8th International Conference on Systems and Control (ICSC) 2019 8th International Conference on Systems and Control (ICSC), Oct 2019, Marrakesh, Morocco. pp.423-428, ⟨10.1109/ICSC47195.2019.8950664⟩ |
| DOI: | 10.1109/ICSC47195.2019.8950664⟩ |
| Popis: | Physical measurements and inspection tests of products quality in semiconductor manufacturing processes are carried out on specific equipment that often expensive and usually separated from production, which is time-consuming and production throughput decrease. Instead, predicting quality state using the available processes data has now become possible through mathematical models where this enables quick decision making in regards to product health state based on the prognosticated information about product quality. In this context, data-driven techniques based on Multilayer Perceptron Artificial Neural Network (MLP-ANN) have proposed to reveal the relationship between products end quality state and processes alarm events. Within this framework, data transformation and adaptation have discussed and model parameters selection was accomplished using the cross-validation technique. The results show a reasonable performance of the selected model that was effectively proven on a data-set collected over the whole semiconductor fabrication facility. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|