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| 1 | Optoelectronic memristor for neuromorphic computing显示文摘With the need of the internet of things,big data,and artificial intelligence,creating new computing architecture is greatly desired for handling data-intensive tasks.Human brain can simultaneously process and store information,which would reduce the power consumption while improve the efficiency of computing.Therefore,the development of brainlike intelligent device and the construction of brain-like computation are important breakthroughs in the field of artificial intelligence.Memristor,as the fourth fundamental circuit element,is an ideal synaptic simulator due to its integration of storage and processing characteristics,and very similar activities and the working mechanism to synapses among neurons which are the most numerous components of the brains.In particular,memristive synaptic devices with optoelectronic responding capability have the benefits of storing and processing transmitted optical signals with wide bandwidth,ultrafast data operation speed,low power consumption,and low cross-talk,which is important for building efficient brain-like computing networks.Herein,we review recent progresses in optoelectronic memristor for neuromorphic computing,including the optoelectronic memristive materials,working principles,applications,as well as the current challenges and the future development of the optoelectronic memristor. | 薛武红 次红娟 许小红 刘刚 | 2020 | Chinese Physics B2020，29，4： | 2 |
| 2 | Artificial neural networks based on memristive devices显示文摘The advent of memristive devices and the continuing research and development in the field of neuromorphic computing show great potential as an alternative to traditional von Neumann computing and bring us ever closer to realizing a true 'thinking machine'. Novel neural network architectures and algorithms inspired by the brain are becoming more and more attractive to keep up with computing needs,relying on intrinsic parallelism and reduced power consumption to outperform more conventional computing methods. This article provides an overview of various neural networks with an emphasis on networks based on memristive emerging devices, with the advantages of memristor neural networks compared with pure complementary metal oxide semiconductor(CMOS) implementations. A general description of neural networks is presented, followed by a survey of prominent CMOS networks, and finally networks implemented using emerging memristive devices are discussed, along with the motivation for developing memristor based networks and the computational potential these networks possess. | Vignesh RAVICHANDRAN Can LI Ali BANAGOZAR J.Joshua YANG Qiangfei XIA | 2018 | Science China(Information Sciences)2018，61，6： | 1 |
| 3 | Challenges of memristor based neuromorphic computing system显示文摘The high efficiency of human brain illmninates thedevelopment of neuromorphic computing system(NCS). In truman brain, membrane potential thatgoes beyond certain threshold voltage triggers thepropagation of spike signals that carries the infor-mation to proceeding neurons. This process is usu-ally abstracted as the integration and fire model,inspiring circuit design and architecture solutionsfor neuromorphic computing. Different from con-ventional von Neumann architecture, neuromor-phic systems tend to closely integrate the comput-ing units and memories, efficiently reducing thedistance between computing units and memoryand consequently breaking the so-called "memorywall" . | Bonan YAN Yiran CHEN Hai KI | 2018 | Science China(Information Sciences)2018，61，6： | 1 |
| 4 | Neuromorphic computing with memristive devices显示文摘Technology advances in the last a few decades have resulted in profound changes in our society, from workplaces to living rooms to how we socialize with each other. These changes in turn drive further technology developments, as the exponential growth of data demands ever increasing computing power. However, improvements in computing capacity from device scaling alone is no longer sufficient, and new materials, devices, and architectures likely need to be developed collaboratively to meet present and future computing needs. Specifically, devices that offer co-located memory and computing characteristics,as represented by memristor devices and memristor-based computing systems, have attracted broad interest in the last decade. Besides tremendous appeal in data storage applications, memristors offer the potential for efficient hardware realization of neuromorphic computing architectures that can effectively address the memory and energy walls faced by conventional von Neumann computing architectures. In this review, we evaluate the state-of-the-art in memristor devices and systems, and highlight the potential and challenges of applying such devices and architectures in neuromorphic computing applications. New directions that can lead to general, efficient in-memory computing systems will also be discussed. | Wen MA Mohammed A.ZIDAN Wei D.LU | 2018 | Science China(Information Sciences)2018，61，6： | 0 |
| 5 | Chemical vapor deposition synthesis of two-dimensional freestanding transition metal oxychloride for electronic applications显示文摘Two-dimensional transition metal oxychlorides(MOCl, M = Fe, Cr, V, Ti, Sc) with the metaloxygen plane sandwiched by two layers of chloride ions possess many exotic physical properties. Nevertheless,it is of great challenge to grow two-dimensional single-crystal MOCl because polyvalent nature of transition metal elements usually gives rise to mixed oxyhalides compounds with distinct physical properties. Here, we take VOCl as an example to present a solution for synthesizing 2 D freestanding MOCl with various thicknesses through chemical vapor deposition(CVD) method. The single crystal and elementary composition as well as elements ratio of as-grown samples have been characterized through measurements of X-ray diffraction, X-ray photoelectron spectroscopy and energy-dispersive spectroscopy, respectively. Furthermore, we demonstrate that 2 D VOCl-based memristive devices show low power consumption and excellent device reliability due to the layered-structure and electrically insulating properties of 2 D VOCl flakes. Besides, we utilize the feature of multilevel resistive switching that memristive devices exhibit to emulate depression and potentiation of synaptic plasticity. This method developed in this study may open up a new avenue for the growth of 2 D MOCl with single crystal and pave the way for high-performance electronic applications. | Shengnan YAN Pengfei WANG Chen-Yu WANG Tao XU Zhuan LI Tianjun CAO Moyu CHEN Chen PAN Bin CHENG Litao SUN Shi-Jun LIANG Feng MIAO | 2019 | Science China(Information Sciences)2019，62，12： | 0 |
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