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	1. Resistance switching characteristic of Ag/Fe2O3/MoS2/Ag with very low switching voltage
	SHU Haiyan,HE Chaotao,ZHANG Xingwen,LI Shichang,CHEN Peng
	Physics 28 November 2023
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	Abstract:In this paper,the resistive switching characteristics of Ag/Fe2O3/MoS2/Ag multilayer film deposited on ITO by magnetron sputtering are investigated.The Ag/Fe2O3/MoS2/Ag device exhibits superior resistive switching behavior compared to the device without Fe2O3 layer due to the positive effect of oxygen vacancies in Fe2O3 on the formation of conducting filaments. The resistive switching ratio of the device is close to 7.0 × 105. The current value of the device drops sharply at 0.12 V when the voltage is swept forward, and the device switches from HRS back to LRS at -0.28 V when a voltage of opposite polarity is applied.The I-V curves of the device are fitted in double logarithmic coordinates, and it is found that the device is controlled by an ohmic conduction model in the low resistance state and two conduction models in the high resistance state: in the low bias region, which exhibits ohmic conduction, and at higher voltages, which is controlled by the SCLC conduction model. Such a resistive switching characteristic with very low switching voltage and high resistance ratio is of particular importance in the application of resistive stochastic storage.
	TO cite this article:SHU Haiyan,HE Chaotao,ZHANG Xingwen, et al. Resistance switching characteristic of Ag/Fe2O3/MoS2/Ag with very low switching voltage[OL].[28 November 2023] http://en.paper.edu.cn/en_releasepaper/content/4761573


	2. Physics-informed Neural Network method for predicting soliton dynamics supported by complex PT-symmetric potentials
	LIU Ximeng,ZHANG Zhiyang,LIU Wenjun
	Physics 06 May 2023
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	Abstract:We examine the deep learning technique referred to as the physics-informed neural network method for approximating nonlinear Schr?dinger equation under considered parity time symmetric potentials and obtaining multifarious soliton solutions. For the first time, neural networks founded principally physical information are adopted to figure out the solution the examined nonlinear partial differential equation and generate six different types of soliton solutions, which are basic, dipole, tripole, quadruple, pentapole and sextupole solitons we consider. We make comparisons between the predicted and actual soliton solutions to see whether deep learning is capable of seeking the solution the partial differential equation described before. We may assess whether physics-informed neural network is capable of effectively providing approximate soliton solutions through the evaluation of squared error between the predicted and numerical results. Besides, we also scrutinize how different activation mechanisms and network architectures impact the capability of selected deep learning technique works.Through the findings we can prove that the neural networks model we established can be utilized to accurately and effectively approximate nonlinear Schr?dinger equation under consideration and predict the dynamics of soliton solution.
	TO cite this article:LIU Ximeng,ZHANG Zhiyang,LIU Wenjun. Physics-informed Neural Network method for predicting soliton dynamics supported by complex PT-symmetric potentials[OL].[ 6 May 2023] http://en.paper.edu.cn/en_releasepaper/content/4760642


	3. Noise Resistance of Next Generation Reservoir Computing: A Comparative Study with High-Order Correlation Computation
	LIU Sheng-Yu,XIAO Jing-Hua,YAN Zi-Xiang,GAO Jian
	Physics 01 April 2023
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	Abstract:Reservoir computing (RC) methods have received more and more attention and applications in chaotic time series prediction with their simple structure and training method. Recently, the next generation reservoir computing (NG-RC) method (Nature Communications,12,5564) has been proposed with less training cost and better time-series predictions. Nevertheless, in practice, available data on dynamic systems are contaminated with noise. Though NG-RC is shown highly efficient in learning and predicting, its noise resistance captivity is not clear yet, limiting its use in practical problems. In this paper, we study the noise resistance of the NG-RC method, taking the well-known denoising method, the high-order correlation computation (HOCC) method, as a reference. Both methods have similar procedures in respect of function bases and regression processes. With the simple ridge regression method, the NG-RC method has a strong noise resistance for white noise, even better than the HOCC method. Besides, the NG-RC method also shows a good prediction ability for small color noise, while it does not provide correct reconstruct dynamics. In this paper, other than reconstruction parameters, four numerical indicators are used to check the noise resistance comprehensively, such as the training error, prediction error, prediction time, and auto-correlation prediction error, for both the short-time series and long climate predictions. Our results provide a systematic estimation of NG-RC's noise resistance capacity, which is helpful for its applications in practical problems.
	TO cite this article:LIU Sheng-Yu,XIAO Jing-Hua,YAN Zi-Xiang, et al. Noise Resistance of Next Generation Reservoir Computing: A Comparative Study with High-Order Correlation Computation[OL].[ 1 April 2023] http://en.paper.edu.cn/en_releasepaper/content/4759954


	4. Quantum Transport Simulations of a Proposed Logic In-Memory Device Based on Bipolar Magnetic Semiconductor
	Ke Yunzhe,Yin Guoxue,Zhang Lingxue,Li Wei,Quhe Ruge
	Physics 20 March 2023
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	Abstract:To overcome the memory wall based on the von Neumann architecture, in-memory computing has been inten

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