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Learn.: Sci. Technol."],"published-print":{"date-parts":[[2021,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>This paper discusses two cases of applying artificial neural networks to the capacitance\u2013voltage characteristics of InAsSb-based barrier infrared detectors. In the first case, we discuss a methodology for training a fully-connected feedforward network to predict the capacitance of the device as a function of the absorber, barrier, and contact doping densities, the barrier thickness, and the applied voltage. We verify the model\u2019s performance with physics-based justification of trends observed in single parameter sweeps, partial dependence plots, and two examples of gradient-based sensitivity analysis. The second case focuses on the development of a convolutional neural network that addresses the inverse problem, where a capacitance\u2013voltage profile is used to predict the architectural properties of the device. The advantage of this approach is a more comprehensive characterization of a device by capacitance\u2013voltage profiling than may be possible with other techniques. Finally, both approaches are material and device agnostic, and can be applied to other semiconductor device characteristics.<\/jats:p>","DOI":"10.1088\/2632-2153\/abcf89","type":"journal-article","created":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T22:45:31Z","timestamp":1606862731000},"page":"025006","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Machine learning for analyzing and characterizing InAsSb-based nBn photodetectors"],"prefix":"10.1088","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1179-3240","authenticated-orcid":false,"given":"Andreu","family":"Glasmann","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8916-3791","authenticated-orcid":false,"given":"Alexandros","family":"Kyrtsos","sequence":"additional","affiliation":[]},{"given":"Enrico","family":"Bellotti","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2020,12,29]]},"reference":[{"key":"mlstabcf89bib1","author":"Goodfellow","year":"2016"},{"key":"mlstabcf89bib2","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: trends, perspectives and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"mlstabcf89bib3","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"mlstabcf89bib4","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1093\/bib\/bbk007","article-title":"Machine learning in bioinformatics","volume":"7","author":"Larra\u00f1aga","year":"2006","journal-title":"Briefings Bioinform."},{"key":"mlstabcf89bib5","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.commatsci.2016.12.004","article-title":"Multi-fidelity machine learning models for accurate bandgap predictions of solids","volume":"129","author":"Pilania","year":"2017","journal-title":"Comput. 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