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| 1 | Nanoscale resistive switching devices for memory and computing applications显示文摘With the slowing down of the Moore’s law and fundamental limitations due to the von-Neumann bottleneck,continued improvements in computing hardware performance become increasingly more challenging.Resistive switching(RS)devices are being extensively studied as promising candidates for next generation memory and computing applications due to their fast switching speed,excellent endurance and retention,and scaling and three-dimensional(3D)stacking capability.In particular,RS devices offer the potential to natively emulate the functions and structures of synapses and neurons,allowing them to efficiently implement neural networks(NNs)and other in-memory computing systems for data intensive applications such as machine learning tasks.In this review,we will examine the mechanisms of RS effects and discuss recent progresses in the application of RS devices for memory,deep learning accelerator,and more faithful brain-inspired computing tasks.Challenges and possible solutions at the device,algorithm,and system levels will also be discussed. | Seung Hwan Lee Xiaojian Zhu Wei D.Lu | 2020 | Nano Research2020，13，5： | 8 |
| 2 | Fabrication and characteristics of nitrogen-doped nanocrystalline diamond/p-type silicon heterojunction显示文摘Nitrogen-doped nanocrystalline diamond films(N-NDFs)have been deposited on p-type silicon(Si)by microwave plasma chemical vapor deposition.The reaction gases are methane,hydrogen,and nitrogen without the conventional argon(Ar).The N-NDFs were characterized by X-ray diffraction,Raman spectroscopy,and scanning electron microscopy.The grain sizes are of 8~10 nm in dimension.The N-NDF shows n-type behavior and the corresponding N-NDF/p-Si heterojunction diodes are realized with a high rectification ratio of 102 at^7.8 V,and the current density reaches to1.35 A/cm2 at forward voltage of 8.5 V.The findings suggest that fabricated by CH_4/H_2/N_2 without Ar,the N-NDFs and the related rectifying diodes are favorable for achieving high performance diamond-based optoelectronic devices. | D.Lu H.D.Li S.H.Cheng J.J.Yuan X.Y.Lv | 2010 | Nano-Micro Letters2010，2，1： | 2 |
| 3 | Neural connectivity inference with spike-timing dependent plasticity network显示文摘Knowing the connectivity patterns in neural circuitry is essential to understand the operating mechanism of the brain,as it allows the analysis of how neural signals are processed and flown through the neural system.With the recent advances in neural recording technologies in terms of channel size and time resolution,a simple and efficient system to perform neural connectivity inference is highly desired,which will enable the process of high dimensional neural activity recording data and reduction of the computational time and cost.In this work,we show that the spike-timing dependent plasticity(STDP)algorithm can be used to reconstruct neural connectivity patterns in a biological neural network,with higher accuracy and efficiency than statistic-based inference methods.The biologically inspired STDP learning rules are natively implemented in a second-order memristor network and are used to estimate the type and the direction of neural connections.When stimulated by the recorded neural spike trains,the memristor device conductance is modulated by the proposed STDP learning rules,which in turn reflects the correlation of the spikes and the possibility of neural connections.By compensating for the different levels of neural activity,highly reliable inference performance can be achieved.The proposed approach offers real-time and local learning,resulting in reduced computational cost/time and strong tolerance to variations of the neural system. | John MOON Yuting WU Xiaojian ZHU Wei D.LU | 2021 | Science China(Information Sciences)2021，64，6： | 2 |
| 4 | Linking Amazonian secondary succession forest growth to soil properties显示文摘 | D.Lu E.Moran P.Mausel | 2002 | Land Degrad Dev2002，，4： | 1 |
| 5 | Strain-specific activation of the NF-kappaB pathway by GRA15,a novel Toxoplasma gondii dense granule protein显示文摘 | E.E.Rosowski D.Lu L Julien L.Rodda R.A.Gaiser K.D.Jensen J.P.Saeij | | 0，，： | 1 |
| 6 | 钢中喂入钙芯线的动力学研究显示文摘钢水钙处理技术正得到广泛应用,但所得到的结果很不稳定,可能是由于炼钢作业的变化造成的,更主要的原因是由于我们没有全面认识到钙的溶解以及钙参与脱硫、脱氧和使氧化物和硫化物发生转变的反应机理。 | D.Lu 王长生 | 1996 | 黑龙江冶金1996，，4： | 0 |
| 7 | 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 |
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