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Title: | Machine Learning on FPGA for Robust Si3N4-Gate ISFET pH Sensor in Industrial IoT Applications |
Authors: | Chamola, Vinay |
Keywords: | EEE SPICE Sensors Temperature sensors Logic gates Task analysis Monitoring |
Issue Date: | Oct-2021 |
Publisher: | IEEE |
Abstract: | This article presents performance enhancement of Si3N4 -gate ion-sensitive field-effect transistor based pH sensor using machine learning (ML) techniques. A robust SPICE macromodel is developed using experimental data, which incorporates intrinsic temperature and temporal characteristics of the device, which is further used in sensor readout circuit (ROIC), which shows a nonideal temperature and time dependence in the voltage output. To make the device robust to the critical drifts, we exploit six state-of-the-art ML models, which are trained using the data generated from ROIC for a wide range of pH, temperature, and temporal conditions. Thorough comparison between ML models shows random forest outperforms other models for drift compensation task. This work also shows a preliminary time series classification task. The ML models are implemented on a Xilinx PYNQ-Z1 field-programmable gate array (FPGA) board to validate the performance in power and memory-restricted environment, crucial for IoT applications. A parameter, implementation factor is defined to evaluate best ML model for IoT deployment using FPGA/MCU hardware implementation. The significantly lower power consumption of FPGA board as compared to CPU with no noticeable performance drop is a pointer to the future of robust pH sensors used in industrial and remote IoT applications. |
URI: | https://ieeexplore.ieee.org/abstract/document/9556621 http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/9833 |
Appears in Collections: | Department of Electrical and Electronics Engineering |
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