Dynamical memristors for higher-complexity neuromorphic computing
Research on electronic devices and materials is currently driven by both the slowing down
of transistor scaling and the exponential growth of computing needs, which make present …
of transistor scaling and the exponential growth of computing needs, which make present …
Neuro-inspired electronic skin for robots
Touch is a complex sensing modality owing to large number of receptors (mechano, thermal,
pain) nonuniformly embedded in the soft skin all over the body. These receptors can gather …
pain) nonuniformly embedded in the soft skin all over the body. These receptors can gather …
A silicon photonic–electronic neural network for fibre nonlinearity compensation
In optical communication systems, fibre nonlinearity is the major obstacle in increasing the
transmission capacity. Typically, digital signal processing techniques and hardware are …
transmission capacity. Typically, digital signal processing techniques and hardware are …
In-sensor reservoir computing for language learning via two-dimensional memristors
The dynamic processing of optoelectronic signals carrying temporal and sequential
information is critical to various machine learning applications including language …
information is critical to various machine learning applications including language …
Resistive switching materials for information processing
The rapid increase in information in the big-data era calls for changes to information-
processing paradigms, which, in turn, demand new circuit-building blocks to overcome the …
processing paradigms, which, in turn, demand new circuit-building blocks to overcome the …
The future of memristors: Materials engineering and neural networks
K Sun, J Chen, X Yan - Advanced Functional Materials, 2021 - Wiley Online Library
Abstract From Deep Blue to AlphaGo, artificial intelligence and machine learning are
booming, and neural networks have become the hot research direction. However, due to the …
booming, and neural networks have become the hot research direction. However, due to the …
Activity-difference training of deep neural networks using memristor crossbars
Artificial neural networks have rapidly progressed in recent years, but are limited by the high
energy costs required to train them on digital hardware. Emerging analogue hardware, such …
energy costs required to train them on digital hardware. Emerging analogue hardware, such …
Volatile and nonvolatile memristive devices for neuromorphic computing
Ion migration as well as electron transfer and coupling in resistive switching materials
endow memristors with a physically tunable conductance to resemble synapses, neurons …
endow memristors with a physically tunable conductance to resemble synapses, neurons …
Nanostructured perovskites for nonvolatile memory devices
Q Liu, S Gao, L Xu, W Yue, C Zhang, H Kan… - Chemical Society …, 2022 - pubs.rsc.org
Perovskite materials have driven tremendous advances in constructing electronic devices
owing to their low cost, facile synthesis, outstanding electric and optoelectronic properties …
owing to their low cost, facile synthesis, outstanding electric and optoelectronic properties …
Bridging biological and artificial neural networks with emerging neuromorphic devices: fundamentals, progress, and challenges
As the research on artificial intelligence booms, there is broad interest in brain‐inspired
computing using novel neuromorphic devices. The potential of various emerging materials …
computing using novel neuromorphic devices. The potential of various emerging materials …