Selective activation of memristive interfaces in TaO x -based devices by controlling oxygen vacancies dynamics at the nanoscale

Epitaxial ferroelectric memristors integrated with silicon

Neuromorphic computing requires the development of solid-state units able to electrically mimic the behavior of biological neurons and synapses. This can be achieved by developing memristive systems based on ferroelectric oxides. In this work we fabricate and characterize high quality epitaxial BaTiO3-based memristors integrated with silicon. After proving the ferroelectric character of BaTiO3 we tested the memristive response of LaNiO3/BaTiO3/Pt microstructures and found a complex behavior which includes the co-existence of volatile and non-volatile effects, arising from the modulation of the BaTiO3/Pt Schottky interface by the direction of the polarization coupled to oxygen vacancy electromigration to/from the interface. This produces remanent resistance loops with tunable ON/OFF ratio and asymmetric resistance relaxations. These properties might be harnessed for the development of neuromorphic hardware compatible with existing silicon-based technology.

Oxygen vacancy dynamics in Pt/TiOx/TaOy/Pt memristors: exchange with the environment and internal electromigration

Memristors are expected to be one of the key building blocks for the development of new bio-inspired nanoelectronics. Memristive effects in transition metal oxides are usually linked to the electromigration at the nanoscale of charged oxygen vacancies (OV). In this paper we address, for Pt/TiO_x/TaO_y/Pt devices, the exchange of OV between the device and the environment upon the application of electrical stress. From a combination of experiments and theoretical simulations we determine that both TiO_xand TaO_ylayers oxidize, via environmental oxygen uptake, during the electroforming process. Once the memristive effect is stabilized (post-forming behavior) our results suggest that oxygen exchange with the environment is suppressed and the OV dynamics that drives the memristive behavior is restricted to an internal electromigration between TiO_xand TaO_ylayers. Our work provides relevant information for the design of reliable binary oxide memristive devices.

Filamentary and Interface-Type Memristors Based on Tantalum Oxide for Energy-Efficient Neuromorphic Hardware

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.

SPICE Implementation of the Dynamic Memdiode Model for Bipolar Resistive Switching Devices

This paper reports the fundamentals and the SPICE implementation of the Dynamic Memdiode Model (DMM) for the conduction characteristics of bipolar-type resistive switching (RS) devices. Following Prof. Chua’s memristive devices theory, the memdiode model comprises two equations, one for the electron transport based on a heuristic extension of the quantum point-contact model for filamentary conduction in thin dielectrics and a second equation for the internal memory state related to the reversible displacement of atomic species within the oxide film. The DMM represents a breakthrough with respect to the previous Quasi-static Memdiode Model (QMM) since it describes the memory state of the device as a balance equation incorporating both the snapback and snapforward effects, features of utmost importance for the accurate and realistic simulation of the RS phenomenon. The DMM allows simple setting of the initial memory condition as well as decoupled modeling of the set and reset transitions. The model equations are implemented in the LTSpice simulator using an equivalent circuital approach with behavioral components and sources. The practical details of the model implementation and its modes of use are also discussed.