Short-Term to Long-Term Plasticity Transition Behavior of Memristive Devices with Low Power Consumption via Facilitating Ionic Drift of Implanted Lithium

Young Pyo Jeon,Sang Yoon Park,Hak Ji Lee,Eun Jung Lee,Yongbin Bang,Young Joon Yoo

doi:10.3390/electronics10212564

Young Pyo Jeon, Sang Yoon Park + Show 4 more

Open Access

https://doi.org/10.3390/electronics10212564

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Abstract

Recent innovations in information technology have encouraged extensive research into the development of future generation memory and computing technologies. Memristive devices based on resistance switching are not only attractive because of their multi-level information storage, but they also display fascinating neuromorphic behaviors. We investigated the basic human brain’s learning and memory algorithm for “memorizing” as a feature for memristive devices based on Li-implanted structures with low power consumption. A topographical and surface chemical functionality analysis of an Li:ITO substrate was conducted to observe its characterization. In addition, a switching mechanism of a memristive device was theoretically studied and associated with ion migrations into a polymeric insulating layer. Biological short-term and long-term memory properties were imitated with the memristive device using low power consumption.

Highlights

The demand for data processing in computing systems is significantly increasing, as data processing has become more complicated due to the diversity of information types, and since new developments in technology, such as big data, deep learning artificial intelligence (AI), and the internet of things (IoT), are enabling us to access an enormous amount of information in real time
From the atomic force microscopy (AFM) image of 5 × 5 μm, the average surface roughness of the Li:ITO was 97.2 nm as shown in Figure 1c, and the particles were confirmed to be Li, which was supported by X-ray photoelectron microscopy (XPS) analysis, because Li was partially implanted on the surface of the ITO originating from a very low growth rate in thermal evaporation process [28]
For realization of a human brain’s learning and memory algorithm, we demonstrated a transition from short-term memory (STM) to long-term memory (LTM) of the memristive device based on a pulse operating as shown Figure 4b,d

Summary

Introduction

The demand for data processing in computing systems is significantly increasing, as data processing has become more complicated due to the diversity of information types, and since new developments in technology, such as big data, deep learning artificial intelligence (AI), and the internet of things (IoT), are enabling us to access an enormous amount of information in real time. Research on memristive devices employing the connection phenomenon of ion filaments or oxygen vacancy has been conducted by storing information with their conductance states and exhibiting conductivity modulation based on the programming electric field [21,22,23] Rapid diffusive ions, such as Ag+ and Cu2+ , or oxygen vacancy migrate into the insulating medium materials to form a filamentary structure, and silicone-based compounds are implemented as a conductive bridge to effectively devise short-term memory (STM) and long-term memory (LTM) [24,25,26,27]. The key issue involved in the low power consumption of the memristive devices performing STM and LTM is that the switching materials can be ionized and aid in high filamentary connectivity by the applied electric stimulus. We investigated whether our device was able to operate analog data processing based on the frequency domain to mimic the human nervous system

Memristive Devices’ Fabrication

Characterization and Device Performance Measrument

Results and Discussion

Conclusions