Phenol-Assisted Electrochemical Metallization of Peptide-Based Bimodal Memristors AITranslate

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DOI：https://doi.org/10.1021/acsmaterialslett.3c01241

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Neuromorphic computing is an emerging approach designed to handle massive amounts of data, and memristors have gained significant attention as a means of realizing this technology. However, the operation of memristors is inherently stochastic, which compromises device controllability. This unpredictability creates a substantial disparity between the synthetic devices and actual biological systems in terms of the cognitive processing efficiency. To address this challenge, we use bioderived materials to develop a bimodal memristor that is activated by either voltage or protons. This mimics biological systems in which the interactions between electrons and ions play a crucial role in cognitive processing. Furthermore, we investigate the influence of functional groups on the bimodally operating memristor characteristics at the molecular level. Our findings show that the phenolic functional group enhances the ionic conductivity, thereby reducing the power consumption during memristive operation. We believe that this research not only presents a more efficient biorealistic device but also establishes design principles for low-power bimodal memristors.