There is an ongoing effort to fabricate miniature, low cost, sensitive, and selective gas sensors for domestic and industrial uses. This paper presents a miniature combustion-type gas sensor (GMOS) based on a thermal sensor, where a micromachined CMOS–SOI transistor integrated with a catalytic reaction plate acts as a sensing element. This study emphasizes GMOS performance modeling, technological aspects, and sensing-selectivity issues. Two deposition techniques of a Pt catalytic layer suitable for wafer-level processing were compared, magnetron sputtering and nanoparticle inkjet printing. Both techniques have been useful for the fabrication of GMOS sensor, with good sensitivity to ethanol and acetone in the air. However, a printed Pt nanoparticle catalyst provides almost twice as much sensitivity as compared to that of the sputtered catalyst. Moreover, sensing selectivity in the ethanol/acetone gas mixture was demonstrated for the GMOS with a Pt nanoparticle catalyst. These advantages of GMOS allow for the fabrication of a low-cost gas sensor that requires a low power, and make it a promising technology for future smartphones, wearables, and Internet of Things (IoT) applications.

We present a tiny combustion-type gas sensor (named GMOS) fabricated using standard CMOS-SOI-MEMS technology. It is a low-cost thermal sensor with an embedded heater, catalytic layer and suspended transistor as a sensing element. The sensor principle relies on the combustion reaction of the gas that takes place on the catalytic layer. The exothermic combustion leads to a sensor temperature increase, which modifies the transistor current-voltage characteristics. The GMOS is useful for detecting different gases, such as ethanol, acetone and especially ethylene, as well as their mixtures. The sensor demonstrates an excellent sensitivity to ethylene of 40 mV/ppm and selective ethylene detection using nanoparticle catalytic layers of Pt, as well as TiO₂. Along with its low energy consumption, GMOS is a promising technology for low-cost ethylene detection systems at different stages in the food supply chain, and it may help reduce global fruit and vegetable loss and waste.