Travelled to:1 × Italy
1 × Korea
1 × Sweden
1 × Taiwan
1 × USA
Collaborated with:S.F.McLoone A.Beghi S.Pampuri A.B.Johnston P.G.O'Hara A.Schirru C.D.Luca G.D.Nicolao J.Wan Marco Maggipinto Federico Zocco D.Pagano Giuliano Zambonin Fabio Altinier Lorenzo Corso Mattia Sessolo M.Zanon
Talks about:predict (6) manufactur (4) metrolog (4) virtual (4) process (4) semiconductor (3) approach (3) mainten (3) system (3) machin (3)
Person: Gian Antonio Susto
DBLP: Susto:Gian_Antonio
Contributed to:
2015201420132012201120182019Wrote 10 papers:
- CASE-2015-SustoM #approach #machine learning #multi #predict
- Slow release drug dissolution profile prediction in pharmaceutical manufacturing: A multivariate and machine learning approach (GAS, SFM), pp. 1218–1223.
- CASE-2014-PampuriSWJOM #process
- Insight extraction for semiconductor manufacturing processes (SP, GAS, JW, ABJ, PGO, SFM), pp. 786–791.
- CASE-2014-SustoWPZJOM #adaptation #flexibility #machine learning #maintenance #predict
- An adaptive machine learning decision system for flexible predictive maintenance (GAS, JW, SP, MZ, ABJ, PGO, SFM), pp. 806–811.
- CASE-2013-SustoJOM #multi #predict #process
- Virtual metrology enabled early stage prediction for enhanced control of multi-stage fabrication processes (GAS, ABJ, PGO, SFM), pp. 201–206.
- CASE-2013-SustoSPPMB #fault #maintenance #predict
- A predictive maintenance system for integral type faults based on support vector machines: An application to ion implantation (GAS, AS, SP, DP, SFM, AB), pp. 195–200.
- CASE-2012-PampuriSSLBN #multi #process
- Multistep virtual metrology approaches for semiconductor manufacturing processes (SP, AS, GAS, CDL, AB, GDN), pp. 91–96.
- CASE-2012-SustoSPNB #approach
- An information-theory and Virtual Metrology-based approach to Run-to-Run semiconductor manufacturing control (GAS, AS, SP, GDN, AB), pp. 358–363.
- CASE-2011-SustoBL #maintenance #predict
- A Predictive Maintenance System for Silicon Epitaxial Deposition (GAS, AB, CDL), pp. 262–267.
- CASE-2018-ZamboninACSBS
- Soft Sensors for Estimating Laundry Weight in Household Heat Pump Tumble Dryers (GZ, FA, LC, MS, AB, GAS), pp. 774–779.
- CASE-2019-MaggipintoSZM #case study #fault #multi #predict #process #what
- What are the Most Informative Data for Virtual Metrology? A use case on Multi-Stage Processes Fault Prediction (MM, GAS, FZ, SFM), pp. 1796–1801.