ACS Sens. 2018 Mar 23;3(3):709-715. doi: 10.1021/acssensors.8b00044. Epub 2018 Mar 13.
For the past several decades, there is growing demand for the development of low-power gas sensing technology for the selective detection of volatile organic compounds (VOCs), important for monitoring safety, pollution, and healthcare. Here we report the selective detection of homologous alcohols and different functional groups containing VOCs using the electrostatically formed nanowire (EFN) sensor without any surface modification of the device. Selectivity toward specific VOC is achieved by training machine-learning based classifiers using the calculated changes in the threshold voltage and the drain-source on current, obtained from systematically controlled biasing of the surrounding gates (junction and back gates) of the field-effect transistors (FET). This work paves the way for a Si complementary metal-oxide-semiconductor (CMOS)-based FET device as an electrostatically selective sensor suitable for mass production and low-power sensing technology.
在过去的几十年中,人们对开发用于选择性检测挥发性有机化合物(VOC)的低功耗气体传感技术的需求不断增长,这对于监测安全、污染和医疗保健非常重要。在这里,我们报告了使用静电形成的纳米线(EFN)传感器在不对器件进行任何表面改性的情况下选择性检测同系醇和不同官能团的 VOC。通过使用计算得出的阈值电压变化和漏源电流,基于机器学习的分类器实现了对特定 VOC 的选择性,这些变化是通过系统地控制场效应晶体管(FET)的周围栅(结栅和背栅)偏置获得的。这项工作为基于 Si 互补金属氧化物半导体(CMOS)的 FET 器件作为一种静电选择性传感器铺平了道路,该传感器适用于大规模生产和低功耗传感技术。