Despite its ability to draw precise inferences from large and complex datasets, 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 of 325-1100nm, 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 eleven filters. By step-wise elimination of filters with the least contribution toward accuracy, mean estimation accuracy of 99% could be obtained even in a two-filter system. Furthermore, we provide a statistical approach for selecting the best "filter" material for any intended spectral range based on the spectral variation of transmittance within the desired range of wavelengths. And finally, we demonstrate that calibrating the data-driven models for the filters from time to time overcomes the minor drifts in their transmittance values, which allows using the same filters indefinitely. This work not only enables the development of simple cyber-physical photodetectors with high accuracy color-estimation, but also provides a framework for developing similar cyber-physical systems with drastically reduced complexity.
Strong Exciton-Photon Coupling in Large Area MoSe$_2$ and WSe$_2$ Heterostructures Fabricated from Two-Dimensional Materials Grown by Chemical Vapor Deposition
Deep Learning-enabled Detection and Classification of Bacterial Colonies using a Thin Film Transistor (TFT) Image Sensor
University of California, Los Angeles（加州大学洛杉矶分校）；California NanoSystems Institute (CNSI)（加州纳米系统研究所）；Texas A&M University（德州农工大学）；Japan Display Inc.（日本显示公司）
早期发现和鉴定致病菌，如大肠杆菌（E。大肠杆菌）是公共卫生的一项重要任务。用于细菌菌落检测的传统的基于培养的方法通常需要>24小时才能获得最终读数。在这里，我们展示了一种细菌菌落形成单位（CFU）检测系统，该系统利用基于薄膜晶体管（TFT）的图像传感器阵列，与环境保护局（EPA）批准的方法相比，可节省约12小时。为了证明该CFU检测系统的有效性，使用TFT图像传感器构建了无透镜成像模式，样品视场约为10 cm ^2。以5分钟的间隔自动收集在显色琼脂平板上培养的细菌菌落的延时图像。使用两个深度神经网络来检测和计数生长的菌落并识别它们的种类。用265个E.大肠杆菌和其他大肠杆菌（即，柠檬酸杆菌和肺炎克雷伯菌），我们的系统在孵育9小时时达到97.3%的平均CFU检测率，在约12小时时达到91.6%的平均回收率。这种基于TFT的传感器可以应用于各种微生物检测方法。由于基于TFT的图像传感器的大可扩展性、超大视场和低成本，该平台可以与待测试的每个琼脂平板集成，并在自动化CFU计数后进行处理。该平台的成像视场可以成本有效地增加到>100 cm^2，以提供用于CFU检测的大规模通量，例如，如柔性显示器工业中所用的TFT的卷对卷制造。
