Professor, Chemical and Biological Engineering
Semiconductor manufacturing operations are complex, with many processing steps. Data analytics techniques to predict problems early in the process, before finding that the final product does not meet specifications, can save millions of dollars.
Yang S, Bequette BW. (2022). Observational process data analytics using causal inference. AIChE Journal; e17986. doi:10.1002/aic.17986
Yerimah LE, Ghosh S, Wang Y, Cao Y, Flores-Cerrillo J, Bequette BW. (2022) Process prediction and detection of faults using bidirectional recurrent neural networks on real plant data. J. Adv. Manuf. Process; doi:10.1002/amp2.10124.
Ghosh S, Bequette BW. (2020) Process Systems Engineering and the Human-In-the-Loop – The Smart Control Room. Ind. Eng. Chem. Res. 59(6)2422-2429. doi: 10.1021/acs.iecr.9b04739
Bequette BW. (2019) Commentary: The Smart Human in Smart Manufacturing. Ind. Eng. Chem. Res. 58(42):19317-19321. doi:10.1021/acs.iecr.9b03544
Shu Y, Navarathna P, Ghosh S, Bequette BW. (2020) Hybrid modeling in the era of smart manufacturing. Comp. Chem. Engng. 140;106784, doi: 10.1016/j.compchemeng.2020.106874
Setalvad, T., I. Trachtenberg, B.W. Bequette and T.F. Edgar, (1989). Optimization of a Low Pressure CVD Reactor for the Deposition of Thin Films, Ind. Eng. Chem. Res. 28 (8), 1162-1170