The growing use of artificial intelligence (AI)-based models is placing greater demands on the electronics industry, as many of these models require significant storage space and computational power. Engineers worldwide have thus been trying to develop neuromorphic computing systems that could help meet these demands, many of which are based on memristors.
Memristors are electronic components that regulate the flow of electrical current in circuits while also "remembering" the amount of electrical charge that previously passed through them. These components could replicate the function of biological synapses in the human brain, thus improving the efficiency with which machine learning-based models analyze data and perform computations.
Despite their potential, most memristors developed to date have exhibited significant limitations, including small on/off ratios. These small ratios hinder the ability of the memristors to represent precise weights, thus increasing noise and reducing the accuracy of an algorithm's predictions.
Researchers at Wuhan University recently developed promising new memristors with analog switching and high on/off ratios. These memristors, introduced in a paper published in Nature Electronics, were fabricated using two-dimensional (2D) van der Waals metallic materials as cathodes.
"Analog memristors with multiple conductance states are of particular use in high-efficiency neuromorphic computing, but their weight mapping capabilities are typically limited by small on/off ratios," Yesheng Li, Yao Xiaong and their colleagues wrote in their paper.
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