Author Affiliations
Abstract
1 State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, School of Electromechnical Engineering, Guangdong University of Technology, Guangzhou 510006, People’s Republic of China
2 School of Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
3 Guangdong ADA Intelligent Equipment Ltd, Foshan 510006, People’s Republic of China
4 Institute of Business Analysis and Supply Chain Management, College of Management, Shenzhen University, Shenzhen, People’s Republic of China
5 School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
Solid-state nanopores with controllable pore size and morphology have huge application potential. However, it has been very challenging to process sub-10 nm silicon nanopore arrays with high efficiency and high quality at low cost. In this study, a method combining metal-assisted chemical etching and machine learning is proposed to fabricate sub-10 nm nanopore arrays on silicon wafers with various dopant types and concentrations. Through a SVM algorithm, the relationship between the nanopore structures and the fabrication conditions, including the etching solution, etching time, dopant type, and concentration, was modeled and experimentally verified. Based on this, a processing parameter window for generating regular nanopore arrays on silicon wafers with variable doping types and concentrations was obtained. The proposed machine-learning-assisted etching method will provide a feasible and economical way to process high-quality silicon nanopores, nanostructures, and devices. Supplementary material for this article is available online
sub-10 nm silicon nanopore array metal-assisted chemical etching silica-coated gold nanoparticles self-assembly machine learning International Journal of Extreme Manufacturing
2021, 3(3): 035104
Author Affiliations
Abstract
1 Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
2 Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
3 Centre for Novel Biomaterials, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
4 Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
Quantitative phase microscopy (QPM) has emerged as an important tool for material metrology and biological imaging. For broader adoption in those applications, we have proposed and demonstrated a new portable off-axis QPM method, which works in both transmission and reflection modes to meet different sample measurement requirements. The temporal and spatial sensitivities of our system, as quantified by optical path-length difference values, are 0.65 nm and 1.04 nm, respectively. To demonstrate its applicability for a wide range of applications, we deployed our system for profiling transistor gold electrode samples, observing red blood cell membrane fluctuations, imaging living cells flowing in a microfluidic chip, etc. Our portable QPM system has a low-cost design and involves a simple and robust phase-retrieval algorithm that we envision will allow for broader deployment at different environmental settings, including in resource-limited sites and integration with other metrology or imaging modalities.
Photonics Research
2020, 8(7): 07001253
西安建筑科技大学 机电工程学院, 西安 710055
针对油膜厚度的静、动态测量, 设计了一种基于反射式强度调制型同轴结构光纤位移传感器(RLIM-FODS)的精密位移检测系统。根据激光光束光场近似高斯分布的特性建立数学模型, 利用Matlab对特性函数进行仿真分析, 光电转换对部分背景光进行了有效补偿, 对滤波电路进行了失调电压调节和截止频率仿真修正, 并通过实验进行了验证。仿真和实验结果表明: 建立的模型可以有效减小电源波动、微弯损耗等带来的影响, 信号检测电路能够精确地将小信号进行放大, 并且输出信号稳定, 噪声低, 失调小, 线性区间在1200~2700μm的范围内, 灵敏度可以达到6mV/μm, 满足精密检测要求。
光纤位移传感器 同轴结构 光电转换 电路分析 实验 optical fiber displacement sensor coaxial structure photoelectric conversion circuit analysis experiment
