A Programmable Interface Circuit for MOS Gas Sensor Array

• Published in: IEEE sensors journal Vol. 24; no. 16; pp. 25869 - 25878
• Main Authors: Chen, Dongliang, Xie, Dongcheng, Geng, Liang, Yang, Yujie, Wu, Qiuju, Rong, Qian, Xu, Lei, Wu, Feng
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.08.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Analog circuits; Analog front-end (AFE); Channels; Configurations; Data processing; Digital electronics; Electrical measurement; Electronic noses; Gas detectors; gas recognition; gas sensor array; Gas sensors; Generators; Interfaces; Lithium batteries; Metal oxide semiconductors; Microprocessors; programmable; Recognition; Resistors; Sensor arrays; Sensors; Signal processing; Temperature sensors; Thermal runaway; Voltage generators; Voltage measurement
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2024.3422295

In this article, a programmable interface circuit for metal-oxide-semiconductors (MOS) gas sensor array is proposed. Integrated with analog and digital circuits, the interface circuit can realize sensor configuration, signal acquisition, data processing, and recognition results output by embedded with timing and algorithm code. A fully integrated and programmable analog front-end (AFE) circuit is introduced for sensor configuration, such as heating voltage generator, measuring voltage generator, and load resistor selector. A 12-bit SAR ADC supporting multiple channels is used for signal acquisition. Data processing and analysis are completed in a Cortex-M0 processor and recognition results can be output via common digital interfaces. The programmable interface circuit is realized in Nexchip 110 nm embedded flash process platform with an area of <inline-formula> <tex-math notation="LaTeX">2.53 \times 2.73 </tex-math></inline-formula> mm. By introducing this interface circuit, an e-nose system with virtualized 16 channels is built up based on four MOS gas sensors and applied in safety monitoring of automotive power lithium batteries. With k-nearest neighbor classification algorithm embedded in the processor, the e-nose system identified gas types of thermal runaway process and output the current state of lithium battery pack with high accuracy.