Gas sensor arrays are essential for artificial olfaction and broader intelligent sensing systems, yet on-chip recognition of multiple gas species typically necessitates an additive complex fabrication process for sensor customization or substantial computational resources for deep learning. Here, we propose and demonstrate an electrostatically reconfigurable gas-sensing unit based on a carbon nanotube field-effect transistor (CNT FET), in which gas selectivity is achieved by tuning the bottom-gate voltage to modulate the chemical potential of the top-gate sensing layer, without requiring any physical change to sensing material. The fabricated gas-sensing unit exhibits high sensitivities, with responses exceeding 1000% for 5 ppm of SO2, 2 ppm of NO2, and 20% O2 respectively under appropriate working conditions, and enables selective detection through electrostatic modulation. While it shows negligible responses to representative reducing gases, NO2 maintains a measurable response under elevated humidity (64 at 45% RH). The combined field-dependent and time-resolved behavior produces clear separation in PCA and classification accuracies exceeding 90% within 3 min. Integrating arrays of the reconfigurable gas-sensing units via back-end-of-line (BEOL) processing on silicon-based complementary metal-oxide-semiconductor (CMOS) integrated circuits provides a viable pathway toward real-time, high-precision, multitarget gas-sensor systems.
Yang et al. (Mon,) studied this question.