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Transition Metal Dichalcogenide Thin Films for Precise Optical Wavelength Estimation Using Bayesian Inference

Published on Jul 9, 2019
· DOI :10.1021/acsanm.9b00489
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Abstract
Despite its ability to draw precise inferences from large and complex data sets, the use of data analytics in the field of condensed matter and materials sciences—where vast quantities of complex metrology data are regularly generated—has remained surprisingly limited. Specifically, such approaches could dramatically reduce the engineering complexities of devices that directly exploit the physical properties of materials. Here, we present a cyber-physical system for accurately estimating the wavelength of any monochromatic light in the range 325–1100 nm by applying Bayesian inference on the optical transmittance data from a few low-cost, easy-to-fabricate thin film “filters” of layered transition metal dichalcogenides (TMDs) such as MoS2 and WS2. Wavelengths of tested monochromatic light could be estimated with only 1% estimation error over 99% of the stated spectral range, with lowest error values reaching as low as a few ten parts per million (ppm) in a system with only 11 filters. By stepwise eliminati...
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