结构优化的光寻址电位传感器研究进展  

Research progress of light addressable potentiometric sensor with optimized structure

作　　者：檀杰 刘诗斌[1] 罗杰璋 陈颖豪 杜永乾 TAN Jie;LIU Shibin;LUO Jiezhang;CHEN Yinghao;Du Yongqian(School of Electronics and Information,Northwestern Polytechnical University,Xi′an 710072,China)

机构地区：[1]西北工业大学电子信息学院,陕西西安710072

出　　处：《光学精密工程》2022年第16期1924-1942,共19页Optics and Precision Engineering

基　　金：国家自然科学基金项目(No.61873209);深圳市科技创新委员会基础研究资金项目(No.JCYJ20180306171040865)。

摘　　要：光寻址电位传感器(Light Addressable Potentiometric Sensor,LAPS)作为一种电位型半导体场效应传感器,具有灵敏度高、成本低、制备简单等优点,已经发展为无标记生化物质检测和二维生化物质浓度分布可视化分析的重要工具。由于绝缘层中存在陷阱电荷、半导体层光电转换效率低以及系统存在噪声干扰、检测信号串扰等的影响,LAPS的灵敏度、线性度、空间分辨率和测量速度受限制。因此,在结构层面对LAPS进行优化有助于提升检测性能。本文首先介绍了LAPS的原理包括传感器的结构与工作原理,基于LAPS的测量系统以及LAPS的测量模式,进而分析了对LAPS进行结构层面优化所取得的进展及其相关应用,最后总结了提高LAPS检测性能的结构优化要点并对未来研究的突破点和关键点进行了展望。As a potential semiconductor field-effect sensor,the light-addressable potentiometric sensor(LAPS)has advantages such as high sensitivity,low cost,and simple fabrication. Further,it has become a crucial tool for label-free biochemical detection and visualization analysis of two-dimensional biochemical concentration distribution. Due to the presence of trapped charges in the insulator,low photoelectric conversion efficiency of the semiconductor layer,noise interference in the system,and crosstalk between detection signals,the performance of LAPS is limited in terms of sensitivity,linearity,spatial resolution,and measurement speed. Therefore,structural optimization of LAPS helps to improve detection performance. This paper first introduces the principle of LAPS,including the structure and operation principle of the sensor,measurement system based on the LAPS,and the measurement mode of LAPS. Further,this paper analyzes the optimization progress and related application of LAPS in structure. Finally,this paper summarizes the main points regarding the optimization of LAPS structure for improved detection performance and highlights the breakthrough points and key points for future research.

关 键 词：光寻址电位传感器 无标记 二维浓度分布可视化 结构优化 

分 类 号：TH832[机械工程—仪器科学与技术]