18th IEEE Sensors Conference, Montreal, Kanada, 27 - 30 Ekim 2019
We propose a novel approach towards building a low-power multi-material gas sensor array for E-nose applications via selective on-chip annealing of atomic layer deposited (ALD) multilayer metal oxide stacks. Thin heater membranes, arranged into crossbar arrays, can be used for selective on-chip annealing to convert ALD multilayer stacks into a variety of sensing surfaces that can empower E-nose machine learning for detection of variety of gases with desired specificity. This paper demonstrates the feasibility of this novel approach and utilizes pattern recognition algorithms to show a pathway to adapt this sensor platform for integration with CMOS/MEMS technologies.