Achieving a sub-10 nm nanopore array in silicon by metal-assisted chemical etching and machine learning

Chen, Yun; Chen, Yanhui; Long, Junyu; Shi, Dachuang; Chen, Xin; Hou, Maoxiang; Gao, Jian; Liu, Huilong; He, Yunbo; Fan, Bin; Wong, Ching‐Ping; Zhao, Ni

doi:10.1088/2631-7990/abff6a

Cited by 30 publications

(13 citation statements)

References 35 publications

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“…The pattern of nano-hole array was prepared by the nanosphere lithography method, which had been reported in the previous publications. [44][45][46][47] Subsequently, a Au or Pt flim was coated on the patterned SiC sample. Then the residual photoresist or nanosphere was removed by acetone coupled with ultrasonic vibration.…”

Section: Methodsmentioning

confidence: 99%

Anisotropic Charge Transport Enabling High‐Throughput and High‐Aspect‐Ratio Wet Etching of Silicon Carbide

Shi

Chen

et al. 2022

Small Methods

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devices, [13] such as fin field-effect transistor effect based metal-oxide-semiconductor field-effect transistor [14,15] and insulated gate bipolar transistors (IGBTs), [16,17] which are used in high-speed railways [18,19] and electric vehicles. [20] Despite these advantages, the unique properties of SiC also raise several challenges for device fabrication. The SiC oxidation reaction isThe ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/smtd.202200329.

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Section: Methodsmentioning

confidence: 99%

Anisotropic Charge Transport Enabling High‐Throughput and High‐Aspect‐Ratio Wet Etching of Silicon Carbide

Shi

Chen

et al. 2022

Small Methods

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“…The correlation between the internet search index and patient arrivals was verified by Pearson correlation coefficient, Johansen cointegration, and Granger causality analysis. We then applied 8 forecasting models to predict ED patient arrivals, including ELM, generalized linear model (GLM), autoregressive integrated moving average model (ARIMA), ARIMA with explanatory variables (ARIMAX), support vector machine (SVM), artificial neural network (ANN), random forest (RF), and long short-term memory (LSTM) [24][25][26][27][28][29][30][31][32][33]. After that, their performances were evaluated in terms of accuracy and robustness analysis.…”

Section: Overviewmentioning

confidence: 99%

Accurate Forecasting of Emergency Department Arrivals With Internet Search Index and Machine Learning Models: Model Development and Performance Evaluation

Fan¹,

Peng²,

Guo³

et al. 2022

JMIR Med Inform

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Background Emergency department (ED) overcrowding is a concerning global health care issue, which is mainly caused by the uncertainty of patient arrivals, especially during the pandemic. Accurate forecasting of patient arrivals can allow health resource allocation in advance to reduce overcrowding. Currently, traditional data, such as historical patient visits, weather, holiday, and calendar, are primarily used to create forecasting models. However, data from an internet search engine (eg, Google) is less studied, although they can provide pivotal real-time surveillance information. The internet data can be employed to improve forecasting performance and provide early warning, especially during the epidemic. Moreover, possible nonlinearities between patient arrivals and these variables are often ignored. Objective This study aims to develop an intelligent forecasting system with machine learning models and internet search index to provide an accurate prediction of ED patient arrivals, to verify the effectiveness of the internet search index, and to explore whether nonlinear models can improve the forecasting accuracy. Methods Data on ED patient arrivals were collected from July 12, 2009, to June 27, 2010, the period of the 2009 H1N1 pandemic. These included 139,910 ED visits in our collaborative hospital, which is one of the biggest public hospitals in Hong Kong. Traditional data were also collected during the same period. The internet search index was generated from 268 search queries on Google to comprehensively capture the information about potential patients. The relationship between the index and patient arrivals was verified by Pearson correlation coefficient, Johansen cointegration, and Granger causality. Linear and nonlinear models were then developed with the internet search index to predict patient arrivals. The accuracy and robustness were also examined. Results All models could accurately predict patient arrivals. The causality test indicated internet search index as a strong predictor of ED patient arrivals. With the internet search index, the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the linear model reduced from 5.3% to 5.0% and from 24.44 to 23.18, respectively, whereas the MAPE and RMSE of the nonlinear model decreased even more, from 3.5% to 3% and from 16.72 to 14.55, respectively. Compared with each other, the experimental results revealed that the forecasting system with extreme learning machine, as well as the internet search index, had the best performance in both forecasting accuracy and robustness analysis. Conclusions The proposed forecasting system can make accurate, real-time prediction of ED patient arrivals. Compared with the static traditional variables, the internet search index significantly improves forecasting as a reliable predictor monitoring continuous behavior trend and sudden changes during the epidemic (P=.002). The nonlinear model performs better than the linear counterparts by capturing the dynamic relationship between the index and patient arrivals. Thus, the system can facilitate staff planning and workflow monitoring.

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“…With the continuous progress of the process in recent years, especially the achievements in the nanometer order of magnitude, the maturity and stability of etching have been improved [ 24 , 25 , 26 ]. This brings great convenience to the production of traditional 3D electrode detectors.…”

Section: Introductionmentioning

confidence: 99%

Design and Simulated Electrical Properties of a Proposed Implanted-Epi Silicon 3D-Spherical Electrode Detector

Cai

et al. 2023

Micromachines

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A new type of 3D electrode detector, named here as the Implanted-Epi Silicon 3D-Spherical Electrode Detector, is proposed in this work. Epitaxial and ion implantation processes can be used in this new detector, allowing bowl-shaped electrodes to penetrate the silicon completely. The distance between the bowl cathode and the central collection electrode is basically the same, thus the total depletion voltage of Implanted-Epi Silicon 3D-Spherical Electrode Detectors is no longer directively correlated with the thickness of the silicon wafer, but only related to the electrode spacing. In this work, we model the device physics of this new structure and use a simulation program to conduct a systematic 3D simulation of its electrical characteristics, including electric potential and electric field distributions, electron concentration profile, leakage current, and capacitance, and compare it to the traditional 3D detectors. The theoretical and simulation study found that the internal electric potential of the new detector was smooth and no potential saddle point was found. The electric field is also uniform, and there is no zero field and a low electric field area. Compared with the traditional silicon 3D electrode detectors, the full depletion voltage is greatly reduced and the charge collection efficiency is improved. As a large electrode spacing (up to 500 μm) can be realized in the Implanted-Epi Silicon 3D-Spherical Electrode Detector thanks to their advantage of a greatly reduced full depletion voltage, detectors with large pixel cells (and thus small dead volume) can be developed for applications in photon science (X-ray, among others).

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Achieving a sub-10 nm nanopore array in silicon by metal-assisted chemical etching and machine learning

Cited by 30 publications

References 35 publications

Anisotropic Charge Transport Enabling High‐Throughput and High‐Aspect‐Ratio Wet Etching of Silicon Carbide

Anisotropic Charge Transport Enabling High‐Throughput and High‐Aspect‐Ratio Wet Etching of Silicon Carbide

Accurate Forecasting of Emergency Department Arrivals With Internet Search Index and Machine Learning Models: Model Development and Performance Evaluation

Design and Simulated Electrical Properties of a Proposed Implanted-Epi Silicon 3D-Spherical Electrode Detector

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