Selectivity enhancement of gas sensitive field effect transistors by dynamic operation

Více o knize

Gas sensitive field effect transistors based on silicon carbide (SiC-FETs) have been utilized primarily for exhaust and combustion monitoring. Traditionally, these sensors operate at constant temperatures, with adaptations made through material and transducer platform optimization. This thesis systematically develops a methodology for dynamic operation to enhance selectivity in SiC-FETs. It introduces temperature cycling, a technique familiar in metal oxide gas sensors, and incorporates gate bias modulation to further improve performance. The multi-dimensional sensor data is analyzed using pattern recognition techniques grounded in multivariate statistics, exploring various strategies for feature selection, cross-validation, and classification methods. The dynamic operation methodology, applying a virtual multi-sensor approach to SiC-FETs, is validated through two laboratory case studies: one focusing on the discrimination of typical exhaust gases and quantification of nitrogen oxides in varying backgrounds, and another on the discrimination and quantification of volatile organic compounds in the low parts-per-billion range for indoor air quality. The selectivity of SiC-FETs is further enhanced by combining temperature and gate bias cycling, while stability is improved through extended training.