  We present a monolithic, microfabricated, metal-oxide semiconductor (MOS)
sensor array in conjunction with a machine learning algorithm to determine
unique fingerprints of individual gases within homogenous mixtures. The array
comprises four different metal oxides, and is engineered for independent
temperature control and readout from each individual pixel in a multiplexed
fashion. The sensor pixels are designed on a very thin membrane to minimize
heat dissipation, thereby significantly lowering the overall power consumption
($<$30 $\mu$W average power). The high dimensional data obtained by running the
pixels at different temperatures, is used to train our machine learning
algorithm with an average accuracy $\sim$ 88$\%$ for high resolution detection
and estimation of concentration of individual constituents in a homogenous
mixture. While the response of MOS sensors to various gases has been
demonstrated, very few studies have investigated the response of these sensors
to homogeneous mixtures of gases comprising several gases. We demonstrate this
principle for a binary homogeneous mixture of ozone and carbon monoxide, both
of which are criteria pollutant gases. Our findings indicate that a
multiplicity of MOS elements together with the ability to vary and measure at
various temperatures are essential in predicting concentration of individual
gases within mixtures, thereby overcoming a key limitation of MOS sensors -
poor selectivity. The small form-factor and microfabrication approach of our
sensor array also lends itself to CMOS integration paving the way for a
platform for wearable and portable applications.
