journal article·2021-08-25Open access

Boost of the Bio-memristor Performance for Artificial Electronic Synapses by Surface Reconstruction

Jingjuan Wang, Chenyang Shi, Maria L. Sushko, Jinling Lan, Kaixuan Sun, Jianhui Zhao, XiangYang Liu, Xiaobing Yan

ACS Applied Materials & Interfaces· Volume 13· Issue 33

Abstract

Biomaterial-based memristors (bio-memristors) are often adopted to emulate biological synapse functions and applied to construct neural computing networks in brain-inspired chip systems. However, the randomness of conductive filament formation in bio-memristors inhibits their switching performance by causing the dispersion of the device-switching parameters. In this case, a facile porous silk fibroin (p-SF) memristor was obtained through a protein surface reconstruction strategy, in which the size of the hole can be adjusted by the density of hybrid nanoseeds. The porous SF memristors exhibit greatly enhanced electrical characteristics, including uniform I–V cycles, centralized distribution of the switching voltages, and both high and low resistances, compared to devices without pores. The results of three-dimensional (3D) simulations based on classical density functional theory (cDFT) suggest that the reconstructed pores in the SF layers guide the formation and fracture of Ag filaments under an electric field and enhance the overall conductivity by separating Ag+ ion and electron diffusion pathways. Ag+ ions are predicted to preferentially diffuse through pores, whereas electrons diffuse through the SF network. Interestingly, the device conductance can be bidirectionally modulated gradually by positive and negative voltages, can faithfully simulate short-term and long-term plasticity, and can even realize the triplet-spike-timing-dependent plasticity (triplet-STDP) rule, which can be used for pattern recognition in biological systems. The simulation results reveal that a memristor network of this type has an accuracy of ∼95.78% in memory learning and the capability of pattern learning. This work provides a facile technology route to improve the performance of bionic-material memristors.
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Research context

OpenAlex topics: Advanced Memory and Neural Computing · Neuroscience and Neural Engineering · Photoreceptor and optogenetics research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, China

Department of Chemistry, University of Washington, Seattle, Washington 98195, United States

Research Institute for Biomimetics and Soft Matter, College of Materials, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China

Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States

College of Ocean and Earth Sciences, State Key Laboratory of Marine Environmental Science (MEL), Xiamen University, Xiamen 361005, China

Funding

State Administration of Foreign Experts Affairs · Ministry of Education of the People's Republic of China · National University of Singapore · National Natural Science Foundation of China · Xiamen University · Support Program for the Top Young Talents of Hebei Province · Special project of strategic leading science and technology of Chinese Academy of Sciences · Hundred Persons Plan of Hebei Province · Project of Distinguished Young of Hebei Province · Special support funds for national high level talents · Hebei University graduate innovation funding project in 2021 · outstanding young scientific research and innovation team of Hebei University · Supporting Plan for 100 Excellent Innovative Talents in Colleges and Universities of Hebei Province

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