In-Memory Computing using Memristor Arrays with Ultrathin 2D PdSeO x /PdSe 2 Heterostructure.
Yesheng LiShuai ChenZhigen YuSifan LiYao XiongMer-Er PamYong-Wei ZhangKah Wee AngPublished in: Advanced materials (Deerfield Beach, Fla.) (2022)
In-memory computing based on memristor arrays holds promise to address the speed and energy issues of the classical von Neumann computing system. However, the stochasticity of ions' transport in conventional oxide-based memristors imposes severe intrinsic variability, which compromises learning accuracy and hinders the implementation of neural network hardware accelerators. Here, these challenges are addressed using a low-voltage memristor array based on an ultrathin PdSeO x /PdSe 2 heterostructure switching medium realized by a controllable ultraviolet (UV)-ozone treatment. A distinctively different ions' transport mechanism is revealed in the heterostructure that can confine the formation of conductive filaments, leading to a remarkable uniform switching with low set and reset voltage variability values of 4.8% and -3.6%, respectively. Moreover, convolutional image processing is further implemented using various crossbar kernels that achieve a high recognition accuracy of ≈93.4% due to the highly linear and symmetric analog weight update as well as multiple conductance states, manifesting its potential beyond von Neumann computing.
Keyphrases
- neural network
- working memory
- high density
- quantum dots
- healthcare
- primary care
- aqueous solution
- body mass index
- high resolution
- deep learning
- physical activity
- high efficiency
- weight loss
- high throughput
- metal organic framework
- single cell
- machine learning
- gold nanoparticles
- weight gain
- nitric oxide
- mass spectrometry
- particulate matter
- replacement therapy