Zhongrui Wang
University of Massachusetts Amherst
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Publication
Featured researches published by Zhongrui Wang.
Advanced Materials | 2017
Rivu Midya; Zhongrui Wang; J. W. Zhang; Sergey Savel'ev; Can Li; Mingyi Rao; Moon Hyung Jang; Saumil Joshi; Hao Jiang; Peng Lin; Kate J. Norris; Ning Ge; Qing Wu; Mark Barnell; Zhiyong Li; Huolin L. Xin; R. Stanley Williams; Qiangfei Xia; Jianhua Yang
A novel Ag/oxide-based threshold switching device with attractive features including ≈1010 nonlinearity is developed. High-resolution transmission electron microscopic analysis of the nanoscale crosspoint device suggests that elongation of an Ag nanoparticle under voltage bias followed by spontaneous reformation of a more spherical shape after power off is responsible for the observed threshold switching.
Scientific Reports | 2016
Hao Jiang; Lili Han; Peng Lin; Zhongrui Wang; Moon Hyung Jang; Qing Wu; Mark Barnell; Jianhua Yang; Huolin L. Xin; Qiangfei Xia
Memristive devices are promising candidates for the next generation non-volatile memory and neuromorphic computing. It has been widely accepted that the motion of oxygen anions leads to the resistance changes for valence-change-memory (VCM) type of materials. Only very recently it was speculated that metal cations could also play an important role, but no direct physical characterizations have been reported yet. Here we report a Ta/HfO2/Pt memristor with fast switching speed, record high endurance (120 billion cycles) and reliable retention. We programmed the device to 24 discrete resistance levels, and also demonstrated over a million (220) epochs of potentiation and depression, suggesting that our devices can be used for both multi-level non-volatile memory and neuromorphic computing applications. More importantly, we directly observed a sub-10 nm Ta-rich and O-deficient conduction channel within the HfO2 layer that is responsible for the switching. This work deepens our understanding of the resistance switching mechanism behind oxide-based memristive devices and paves the way for further device performance optimization for a broad spectrum of applications.
Nature Communications | 2017
Hao Jiang; Daniel Belkin; Sergey Savel’ev; Siyan Lin; Zhongrui Wang; Yunning Li; Saumil Joshi; Rivu Midya; Can Li; Mingyi Rao; Mark Barnell; Qing Wu; Jianhua Yang; Qiangfei Xia
The intrinsic variability of switching behavior in memristors has been a major obstacle to their adoption as the next generation of universal memory. On the other hand, this natural stochasticity can be valuable for hardware security applications. Here we propose and demonstrate a novel true random number generator utilizing the stochastic delay time of threshold switching in a Ag:SiO2 diffusive memristor, which exhibits evident advantages in scalability, circuit complexity, and power consumption. The random bits generated by the diffusive memristor true random number generator pass all 15 NIST randomness tests without any post-processing, a first for memristive-switching true random number generators. Based on nanoparticle dynamic simulation and analytical estimates, we attribute the stochasticity in delay time to the probabilistic process by which Ag particles detach from a Ag reservoir. This work paves the way for memristors in hardware security applications for the era of the Internet of Things.Memristors can switch between high and low electrical-resistance states, but the switching behaviour can be unpredictable. Here, the authors harness this unpredictability to develop a memristor-based true random number generator that uses the stochastic delay time of threshold switching
Nature Communications | 2018
Can Li; Daniel Belkin; Yunning Li; Peng Yan; Miao Hu; Ning Ge; Hao Jiang; Eric Montgomery; Peng Lin; Zhongrui Wang; Wenhao Song; John Paul Strachan; Mark Barnell; Qing Wu; R. Stanley Williams; Jianhua Yang; Qiangfei Xia
Memristors with tunable resistance states are emerging building blocks of artificial neural networks. However, in situ learning on a large-scale multiple-layer memristor network has yet to be demonstrated because of challenges in device property engineering and circuit integration. Here we monolithically integrate hafnium oxide-based memristors with a foundry-made transistor array into a multiple-layer neural network. We experimentally demonstrate in situ learning capability and achieve competitive classification accuracy on a standard machine learning dataset, which further confirms that the training algorithm allows the network to adapt to hardware imperfections. Our simulation using the experimental parameters suggests that a larger network would further increase the classification accuracy. The memristor neural network is a promising hardware platform for artificial intelligence with high speed-energy efficiency.Memristor-based neural networks hold promise for neuromorphic computing, yet large-scale experimental execution remains difficult. Here, Xia et al. create a multi-layer memristor neural network with in-situ machine learning and achieve competitive image classification accuracy on a standard dataset.
Nature Materials | 2017
Zhongrui Wang; Saumil Joshi; Sergey Savel’ev; Hao Jiang; Rivu Midya; Peng Lin; Miao Hu; Ning Ge; John Paul Strachan; Zhiyong Li; Qing Wu; Mark Barnell; Geng Lin Li; Huolin L. Xin; R. Stanley Williams; Qiangfei Xia; Jianhua Yang
Nature Electronics | 2018
Can Li; Miao Hu; Yunning Li; Hao Jiang; Ning Ge; Eric Montgomery; J. W. Zhang; Wenhao Song; Noraica Davila; Catherine Graves; Zhiyong Li; John Paul Strachan; Peng Lin; Zhongrui Wang; Mark Barnell; Qing Wu; R. Stanley Williams; Jianhua Yang; Qiangfei Xia
Nature Electronics | 2018
Zhongrui Wang; Saumil Joshi; Sergey Savel’ev; Wenhao Song; Rivu Midya; Yunning Li; Mingyi Rao; Peng Yan; Shiva Asapu; Ye Zhuo; Hao Jiang; Peng Lin; Can Li; Jung Ho Yoon; Navnidhi K. Upadhyay; J. W. Zhang; Miao Hu; John Paul Strachan; Mark Barnell; Qing Wu; Huaqiang Wu; R. Stanley Williams; Qiangfei Xia; Jianhua Yang
Nanoscale | 2016
Zhongrui Wang; Hao Jiang; Moon Hyung Jang; Peng Lin; Alexander E. Ribbe; Qiangfei Xia; Jianhua Yang
Advanced Functional Materials | 2018
Zhongrui Wang; Mingyi Rao; Rivu Midya; Saumil Joshi; Hao Jiang; Peng Lin; Wenhao Song; Shiva Asapu; Ye Zhuo; Can Li; Huaqiang Wu; Qiangfei Xia; Jianhua Yang
Advanced Functional Materials | 2017
Jung Ho Yoon; J. W. Zhang; Xiaochen Ren; Zhongrui Wang; Huaqiang Wu; Zhiyong Li; Mark Barnell; Qing Wu; Lincoln J. Lauhon; Qiangfei Xia; Jianhua Yang