Machine Learning-Based Modeling of pH-Sensitive Silicon Nanowire (SiNW) for Ion Sensitive Field Effect Transistor (ISFET) Article Swipe

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Nabil Ayadi , Ahmet Lale , Bekkay Hajji , Jérôme Launay , Pierre Temple‐Boyer ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.3390/s24248091

The development of ion-sensitive field-effect transistor (ISFET) sensors based on silicon nanowires (SiNW) has recently seen significant progress, due to their many advantages such as compact size, low cost, robustness and real-time portability. However, little work has been done to predict the performance of SiNW-ISFET sensors. The present study focuses on predicting the performance of the silicon nanowire (SiNW)-based ISFET sensor using four machine learning techniques, namely multilayer perceptron (MLP), nonlinear regression (NLR), support vector regression (SVR) and extra tree regression (ETR). The proposed ML algorithms are trained and validated using experimental measurements of the SiNW-ISFET sensor. The results obtained show a better predictive ability of extra tree regression (ETR) compared to other techniques, with a low RMSE of 1 × 10−3 mA and an R2 value of 0.9999725. This prediction study corrects the problems associated with SiNW -ISFET sensors.

Related Topics

Materials Science

Silicon

Transistor

Support Vector Machine

Electronic Engineering

Computer Science

Nanotechnology

Artificial Intelligence

Optoelectronics

Electrical Engineering

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ISFET Materials science Field-effect transistor Robustness (evolution) Nanowire Silicon Transistor Support vector machine Software portability CMOS Electronic engineering Computer science Nanotechnology Artificial intelligence Optoelectronics Electrical engineering Engineering Chemistry Voltage Biochemistry Gene Programming language

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Type: article
Language: en
Landing Page: https://doi.org/10.3390/s24248091
PDF: https://www.mdpi.com/1424-8220/24/24/8091/pdf?version=1734580944
OA Status: gold
Cited By: 2
References: 48
Related Works: 10
OpenAlex ID: https://openalex.org/W4405574479

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DOI: https://doi.org/10.3390/s24248091

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Title: Machine Learning-Based Modeling of pH-Sensitive Silicon Nanowire (SiNW) for Ion Sensitive Field Effect Transistor (ISFET)

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Language: en

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Publication year: 2024

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Publication date: 2024-12-18

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Authors: Nabil Ayadi, Ahmet Lale, Bekkay Hajji, Jérôme Launay, Pierre Temple‐Boyer

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Landing page: https://doi.org/10.3390/s24248091

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Concepts: ISFET, Materials science, Field-effect transistor, Robustness (evolution), Nanowire, Silicon, Transistor, Support vector machine, Software portability, CMOS, Electronic engineering, Computer science, Nanotechnology, Artificial intelligence, Optoelectronics, Electrical engineering, Engineering, Chemistry, Voltage, Biochemistry, Gene, Programming language

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Cited by: 2

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