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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 DBLP: Susto:Gian_Antonio

Contributed to:

CASE 20152015
CASE 20142014
CASE 20132013
CASE 20122012
CASE 20112011
CASE 20182018
CASE 20192019

Wrote 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.
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.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
Hosted as a part of SLEBOK on GitHub.