Ayadi Nabil, Lale Ahmet, Hajji Bekkay, Launay Jérôme, Temple-Boyer Pierre
Laboratory of Energy, Embedded System and Information Processing, National School of Applied Sciences, Mohammed First University, Oujda 60000, Morocco.
CNRS, LAAS, 7 Avenue du Colonel Roche, F-31400 Toulouse, France.
Sensors (Basel). 2024 Dec 18;24(24):8091. doi: 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 mA and an R value of 0.9999725. This prediction study corrects the problems associated with SiNW -ISFET sensors.
基于硅纳米线（SiNW）的离子敏感场效应晶体管（ISFET）传感器的发展近来取得了显著进展，这得益于其诸多优点，如尺寸紧凑、成本低、坚固耐用以及具备实时便携性。然而，在预测SiNW - ISFET传感器的性能方面所做的工作甚少。本研究聚焦于使用四种机器学习技术，即多层感知器（MLP）、非线性回归（NLR）、支持向量回归（SVR）和极端随机树回归（ETR）来预测基于硅纳米线（SiNW）的ISFET传感器的性能。所提出的机器学习算法通过SiNW - ISFET传感器的实验测量数据进行训练和验证。获得的结果表明，与其他技术相比，极端随机树回归（ETR）具有更好的预测能力，其均方根误差（RMSE）低至1×10 mA，R值为0.9999725。这项预测研究纠正了与SiNW - ISFET传感器相关的问题。
