With the rise of Machine Learning (ML) and Artificial Intelligence (AI), the semiconductor industry is undergoing a revolution in how it approaches manufacturing. The SMART-IC project (DATE'24 MPP category: initial stage) works in this direction, by proposing an AI-enabled framework to support the smart monitoring and optimization of the semiconductor manufacturing process. An AI-powered engine examines sensor data recording physical parameters during production (like gas flow, temperature, voltage, etc.) as well as test data, with different goals: (1) the identification of anomalies in the production chain, either offline from collected data-traces or online from a continuous stream of sensed data; (2) the forecasting of new data of the future production; and (3) the automatic generation of synthetic traces, to strengthen the data-based algorithms. All such tasks provide valuable information to an advanced MES, which reacts by optimizing the production process and management of the equipment maintenance policies. SMART-IC is a 300keuro academic project funded by the Italian Ministry of University and supported by STMicroelectronics and Technoprobe with industrial expertise and real-world applications. This paper shares the view of SMART-IC on the future of semiconductor manufacturing, the preliminary efforts, and the future results that will be reached by the end of the project, in 2025.
An AI-Enabled Framework for Smart Semiconductor Manufacturing / Alamin, KHALED SIDAHMED SIDAHMED; Appello, Davide; Beghi, Alessandro; Dall’Ora, Nicola; Depaoli, Fabio; DI CATALDO, Santa; Fummi, Franco; Gaiardelli, Sebastiano; Lora, Michele; Macii, Enrico; Mascolini, Alessio; Pagano, Daniele; Ponzio, Francesco; Antonio Susto, Gian; Vinco, Sara. - ELETTRONICO. - (2024). (Intervento presentato al convegno Design, Automation and Test in Europe Conference (DATE 2024) tenutosi a Valencia (Spain) nel 25 - 27 March 2024).
An AI-Enabled Framework for Smart Semiconductor Manufacturing
Khaled Sidahmed Sidahmed Alamin;Fabio Depaoli;Santa Di Cataldo;Enrico Macii;Alessio Mascolini;Francesco Ponzio;Sara Vinco
2024
Abstract
With the rise of Machine Learning (ML) and Artificial Intelligence (AI), the semiconductor industry is undergoing a revolution in how it approaches manufacturing. The SMART-IC project (DATE'24 MPP category: initial stage) works in this direction, by proposing an AI-enabled framework to support the smart monitoring and optimization of the semiconductor manufacturing process. An AI-powered engine examines sensor data recording physical parameters during production (like gas flow, temperature, voltage, etc.) as well as test data, with different goals: (1) the identification of anomalies in the production chain, either offline from collected data-traces or online from a continuous stream of sensed data; (2) the forecasting of new data of the future production; and (3) the automatic generation of synthetic traces, to strengthen the data-based algorithms. All such tasks provide valuable information to an advanced MES, which reacts by optimizing the production process and management of the equipment maintenance policies. SMART-IC is a 300keuro academic project funded by the Italian Ministry of University and supported by STMicroelectronics and Technoprobe with industrial expertise and real-world applications. This paper shares the view of SMART-IC on the future of semiconductor manufacturing, the preliminary efforts, and the future results that will be reached by the end of the project, in 2025.File | Dimensione | Formato | |
---|---|---|---|
2023_DATE_MPPj_SMART_IC (3).pdf
embargo fino al 04/04/2026
Tipologia:
2. Post-print / Author's Accepted Manuscript
Licenza:
PUBBLICO - Tutti i diritti riservati
Dimensione
621.13 kB
Formato
Adobe PDF
|
621.13 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
An_AI-Enabled_Framework_for_Smart_Semiconductor_Manufacturing.pdf
non disponibili
Tipologia:
2a Post-print versione editoriale / Version of Record
Licenza:
Non Pubblico - Accesso privato/ristretto
Dimensione
867.98 kB
Formato
Adobe PDF
|
867.98 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11583/2987563