Analyzing Interface Trap Influence on Sensitivity, Noise, and Response Time in 2-D Material Field-Effect Transistor pH Sensors: A Theoretical Framework

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Sensors Journal Pub Date : 2024-10-09 DOI:10.1109/JSEN.2024.3472729
S. Sarath;Rajendra P. Shukla;Chandan Yadav;Gopi Krishna Saramekala
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Abstract

This work evaluates the potential impact of interface traps on the interface of oxide and semiconductor on a 2-D-based ion-sensitive field-effect transistor (ISFET) using a surface potential-based model and Technology Computer-Aided Design (TCAD) simulation calibrated for pH sensing applications. The electrolyte/oxide interface in the proposed 2-D ISFET model is modeled using the Guoy-Chapman–Stern technique and the site binding association model. The baseline field-effect transistor (FET) of the ISFET is modeled using Fermi-Dirac statistics to obtain surface potential, which is further used to derive a compact drain current expression. The proposed ISFET model development is carried out by accounting for the interface trap effect. The voltage and current sensitivity of ISFET with and without interface traps are calculated to demonstrate the impact of interface traps in 2-D material-based pH sensors. It is observed that voltage sensitivity remains close to the Nernst limit (59 mV/pH), and a decrease in current sensitivity from 34 to 2 nA/pH is observed, as the density of interface traps is varied from 0 to ${5}\times {10} {^{{12}}}$ cm $^{-}2 $ eV $^{-}1 $ . Another sensitivity metric, the transconductance to drain current ratio, is analyzed for its variation with changes in the density of interface traps. The noise level of ISFET in the presence of interface traps is analyzed, and its influence on the minimum resolvable pH is demonstrated. The proposed model prediction closely matches the TCAD simulation data obtained from a calibrated TCAD simulation setup. The model is suitable for implementation in Verilog-A for ISFET-based circuit simulation, and the impact of interface traps on the response time of ISFET-based circuits is also demonstrated.
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分析界面陷阱对二维材料场效应晶体管 pH 传感器灵敏度、噪声和响应时间的影响:理论框架
这项研究利用基于表面电位的模型和技术计算机辅助设计(TCAD)模拟,评估了氧化物和半导体界面陷阱对基于二维的离子敏感场效应晶体管(ISFET)的潜在影响,并针对 pH 值传感应用进行了校准。在拟议的二维 ISFET 模型中,电解质/氧化物界面采用 Guoy-Chapman-Stern 技术和位点结合关联模型进行建模。ISFET 的基线场效应晶体管 (FET) 采用费米-狄拉克统计法建模,以获得表面电势,并进一步用于推导紧凑的漏极电流表达式。所提出的 ISFET 模型是在考虑了界面陷阱效应的基础上建立的。计算了有界面陷阱和无界面陷阱 ISFET 的电压和电流灵敏度,以证明界面陷阱对基于二维材料的 pH 传感器的影响。结果表明,当界面陷阱的密度从 0 变化到 ${5}\times {10}} 时,电压灵敏度仍然接近于 Nernst 极限(59 mV/pH),而电流灵敏度则从 34 nA/pH 下降到 2 nA/pH。{^{{12}}}$ cm $^{-}2 $ eV $^{-}1 $。另一个灵敏度指标是跨导与漏极电流比,分析了它随界面阱密度变化而变化的情况。分析了存在界面陷阱时 ISFET 的噪声水平,并证明了它对最小可分辨 pH 值的影响。提出的模型预测结果与通过校准 TCAD 仿真设置获得的 TCAD 仿真数据非常吻合。该模型适合在 Verilog-A 中实现,用于基于 ISFET 的电路仿真,同时还证明了接口陷阱对基于 ISFET 电路响应时间的影响。
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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