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Nanochannel‐Based Interfacial Memristor: Electrokinetic Analysis of the Frequency Characteristics
Author(s) -
Chen Kexin,
Tsutsui Makusu,
Zhuge Fuwei,
Zhou Yue,
Fu Yaoyao,
He Yuhui,
Miao Xiangshui
Publication year - 2021
Publication title -
advanced electronic materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.25
H-Index - 56
ISSN - 2199-160X
DOI - 10.1002/aelm.202000848
Subject(s) - memristor , electrokinetic phenomena , materials science , neuromorphic engineering , nanofluidics , microscale chemistry , voltage , nanotechnology , polydimethylsiloxane , interface (matter) , hysteresis , optoelectronics , computer science , electronic engineering , electrical engineering , physics , engineering , condensed matter physics , artificial neural network , artificial intelligence , mathematics education , mathematics , capillary number , capillary action , composite material
As a novel class of memristors, nanochannel‐based interfacial memristors in polydimethylsiloxane offer great flexibility and low cost. With practical attributes of novel transport phenomenon in nanofluidics, analysis of the physical properties and operational mechanisms in such memristor devices is possible. Made of pure soft‐matter materials, this type of device exhibits facile tuning of device conductance via the modification of solution interface positions. Under continuous sweep voltage, a typical hysteresis loop for a memristor can be observed. In this article, the dynamic frequency characteristics of this device is measured, which is regarded as a typical memristor characteristic as previously predicted. However, the physical mechanism behind such memristive behaviors and frequency characteristics is seldomly reported. Generalizing conventional physical models, here a comprehensive model including continuous voltage changes and surface tension modifications is proposed, which is able to predict the interface position changes and explain the conductance switching and frequency property experimentally observed in the device. Provided with a probable explanation of the physical mechanism behind this class of device, the model can serve as a candidate method in designing new nanochannel‐based structures or materials, aiming at more novel functionalities in neuromorphic computation.