To implement artificial neural networks (ANNs) based on memristor devices, it is essential to secure the linearity and symmetry in weight update characteristics of the memristor, and reliability in the cycle-To-cycle and device-To-device variations. This study experimentally demonstrated and compared the filamentary and interface-Type resistive switching (RS) behaviors of tantalum oxide (Ta2O5and TaO2)-based devices grown by atomic layer deposition (ALD) to propose a suitable RS type in terms of reliability and weight update characteristics. Although Ta2O5is a strong candidate for memristor, the filament-Type RS behavior of Ta2O5does not fit well with ANNs demanding analog memory characteristics. Therefore, this study newly designed an interface-Type TaO2memristor and compared it to a filament type of Ta2O5memristor to secure the weight update characteristics and reliability. The TaO2-based interface-Type memristor exhibited gradual RS characteristics and area dependency in both high-and low-resistance states. In addition, compared to the filamentary memristor, the RS behaviors of the TaO2-based interface-Type device exhibited higher suitability for the neuromorphic, symmetric, and linear long-Term potentiation (LTP) and long-Term depression (LTD). These findings suggest better types of memristors for implementing ionic memristor-based ANNs among the two types of RS mechanisms.
|Number of pages||11|
|Journal||ACS Applied Materials and Interfaces|
|Publication status||Published - 2022 Oct 5|
Bibliographical noteFunding Information:
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (grant 2019R1A2C2087604) and Creative Materials Discovery Program through the National Research Foundation of Korea funded by the Ministry of Science and ICT (grant 2018M3D1A1058536). This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2022R1C1C1006337). Experiments at PLS were partly supported by MEST and POSTECH.
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All Science Journal Classification (ASJC) codes
- Materials Science(all)