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.