| Autor: |
Sinha, Soumendu, Sahu, Nishad, Bhardwaj, Rishabh, Mehta, Aditya, Ahuja, Hitesh, Srivastava, Satyam, Elhence, Anubhav, Chamola, Vinay |
| Předmět: |
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| Zdroj: |
IEEE Transactions on Industry Applications; Nov/Dec2021, Vol. 57 Issue 6, p6700-6712, 13p |
| Abstrakt: |
This article presents performance enhancement of ${\rm {Si}}_{3}{\rm {N}}_{4}$ -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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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