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 (Ta2O5 and TaO2)-based devices grown by atomic layer deposition (ALD) to propose a suitable RS type in terms of reliability and weight update characteristics. Although Ta2O5 is a strong candidate for memristor, the filament-type RS behavior of Ta2O5 does not fit well with ANNs demanding analog memory characteristics. Therefore, this study newly designed an interface-type TaO2 memristor and compared it to a filament type of Ta2O5 memristor 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.
Keywords: atomic layer deposition; filament type; interface type; memristor; neuromorphic hardware.