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Much effort has been devoted to device and materials engineering to realize nanoscale resistance random access memory (RRAM) for practical applications, but there still lacks a rational physical basis to be relied on to design scalable…

Mesoscale and Nanoscale Physics · Physics 2017-04-12 Yang Lu , Jong Ho Lee , I-Wei Chen

Neuromorphic networks of artificial neurons and synapses can solve computational hard problems with energy efficiencies unattainable for von Neumann architectures. For image processing, silicon neuromorphic processors outperform graphic…

Emerging Technologies · Computer Science 2018-11-08 Wei Yi , Kenneth K. Tsang , Stephen K. Lam , Xiwei Bai , Jack A. Crowell , Elias A. Flores

Photonic Random-Access Memories (P-RAM) are an essential component for the on-chip non-von Neumann photonic computing by eliminating optoelectronic conversion losses in data links. Emerging Phase Change Materials (PCMs) have been showed…

Passing current at given threshold voltages through a metal/insulator/metal sandwich structure device may change its resistive state. Such resistive switching is unique to nanoscale devices, but its underlying physical mechanism remains…

Memristors are emerging as key electronic components that retain resistance states without power. Their non-volatile nature and ability to mimic synaptic behavior make them ideal for next-generation memory technologies and neuromorphic…

Mesoscale and Nanoscale Physics · Physics 2025-10-28 Tongxin Chen , Yinyu Nie , Yafei Hao , Shengchun Shen , Jiajun Pan , Xiaoguang Li , Yuan Lu

Networks with memristive devices are a potential basis for the next generation of computing devices. They are also an important model system for basic science, from modeling nanoscale conductivity to providing insight into the…

Disordered Systems and Neural Networks · Physics 2025-07-01 Frank Barrows , Forrest C. Sheldon , Francesco Caravelli

Time-dependent drift of resistance and threshold voltage in phase change memory (PCM) devices is of concern as it leads to data loss. Electrical drift in amorphous chalcogenides has been argued to be either due to electronic or stress…

Materials Science · Physics 2010-04-13 Mukut Mitra , Yeonwoong Jung , Daniel S. Gianola , Ritesh Agarwal

The mechanism of resistive switching in two-dimensional (2D) semiconductor-based memristors is intriguing, and our conventional knowledge of bulk-oxide based memristors does not apply to these devices. Experimental data indicate that the…

Materials Science · Physics 2025-10-24 Sanchali Mitra , Santanu Mahapatra

Memristors have been positioned at the forefront of the purposes for carrying out neuromorphic computation. Their tuneable conductivity properties enable the imitation of synaptic behaviour. Multipore nanofluidic memristors have shown their…

Applied Physics · Physics 2024-09-17 Agustin Bou , Patricio Ramirez , Juan Bisquert

The development of novel devices for neuromorphic computing and non-traditional logic operations largely relies on the fabrication of well controlled memristive systems with functionalities beyond standard bipolar behavior and digital…

Applied Physics · Physics 2020-01-29 C. Ferreyra , M. J. Sánchez , M. Aguirre , C. Acha , S. Bengió , J. Lecourt , U. Lüders , D. Rubi

Liquid crystalline polymers are materials of considerable scientific interest and technological value to society [1-3]. An important subset of such materials exhibit rubber-like elasticity; these can combine the remarkable optical…

Electrically driven insulator-metal transitions in prototypical quantum materials such as VO2 offer a foundational platform for designing novel solid-state devices. Tuning the V: O stoichiometry offers a vast electronic phase space with…

Many Prussian Blue Analogues are known to show a thermally induced phase transition close to room temperature and a reversible, photo-induced phase transition at low temperatures. This work reports on magnetic measurements, X-ray…

The memristive device is one of the basic elements of novel, brain-inspired, fast, and energy-efficient information processing systems in which there is no separation between memorization and information analysis functions. Since the first…

Two-dimensional layered semiconductors have recently emerged as attractive building blocks for next-generation low-power non-volatile memories. However, challenges remain in the controllable sub-micron fabrication of bipolar resistively…

Negative differential resistance (NDR) behavior observed in several transition metal oxides is crucial for developing next-generation memory devices and neuromorphic computing systems. NbO2-based memristors exhibit two regions of NDR at…

Mesoscale and Nanoscale Physics · Physics 2024-11-12 Nitin Kumar , Jong E. Han , Karsten Beckmann , Nathaniel Cady , G. Sambandamurthy

In this study, we proposed and demonstrated a self-rectifying property of silicon nitride (Si3N4)-based resistive random access memory device by employing p-type silicon (p-Si) as bottom electrode. The RRAM devices consisted of…

Materials Science · Physics 2016-05-20 Min Ju Yun , Sungho Kim , Hee-Dong Kim

More-versatile memory is strongly desired for end-users to protect their information in the information era. In particular, bit-level switchable memory, from rewritable to read-only function, allows end-users to prevent any important data…

Applied Physics · Physics 2022-08-01 Yu Sheng , Weiyang Wang , Yongcheng Deng , Yang Ji , Houzhi Zheng , Kaiyou Wang

Memristors are non-volatile nano-resistors. Their resistance can be tuned by applied currents or voltages and set to a large number of levels between two limit values. Thanks to these properties, memristors are ideal building blocks for a…

Mesoscale and Nanoscale Physics · Physics 2016-05-26 Steven Lequeux , Joao Sampaio , Vincent Cros , Kay Yakushiji , Akio Fukushima , Rie Matsumoto , Hitoshi Kubota , Shinji Yuasa , Julie Grollier

In the quest for alternatives to traditional CMOS, it is being suggested that digital computing efficiency and power can be improved by matching the precision to the application. Many applications do not need the high precision that is…

Machine Learning · Computer Science 2014-10-17 Juan Pablo Carbajal , Joni Dambre , Michiel Hermans , Benjamin Schrauwen