Understanding Synaptic Mechanisms in SrTiO3 RRAM Devices

In this paper, we investigated the performance of SrTiO3 (STO) resistive random-access memory (RRAM) as a candidate to realize short-term synaptic elements in neuromorphic circuits. Ionic defect movement within oxides is postulated to play a significant role in resistive state reconfiguration of these devices. This relatively small movement in oxides was sensed by developing a novel measurement technique using differential voltammetry. Using this technique, the widely hypothesized belief of drift and diffusion of ionic defects in oxides was systematically studied and linked to the state reconfiguration in STO RRAM. Next, the devices’ state retention was examined and a consistent decay of the resistive state was observed. The decay rate was found to be a function of the conditioning voltage, allowing us to control the state decay via operational voltages. We then developed a test battery that quantified how these devices operate within a spike-rate encoded learning paradigm. By utilizing a carefully tuned test battery, we were able to quantify the magnitude of potentiation and depression due to varying spike amplitudes and spike rates. These tests provide a systematic methodology to capture the constantly changing resistive state of short-term synaptic elements.

[1]  Wei Lu,et al.  Short-term Memory to Long-term Memory Transition in a Nanoscale Memristor , 2022 .

[2]  R. Dittmann,et al.  Redox‐Based Resistive Switching Memories – Nanoionic Mechanisms, Prospects, and Challenges , 2009, Advanced materials.

[3]  Wulfram Gerstner,et al.  A History of Spike-Timing-Dependent Plasticity , 2011, Front. Syn. Neurosci..

[4]  H. Hwang,et al.  Improved Synaptic Behavior Under Identical Pulses Using AlOx/HfO2 Bilayer RRAM Array for Neuromorphic Systems , 2016, IEEE Electron Device Letters.

[5]  Markus Kubicek,et al.  Memristor Kinetics and Diffusion Characteristics for Mixed Anionic‐Electronic SrTiO3‐δ Bits: The Memristor‐Based Cottrell Analysis Connecting Material to Device Performance , 2014 .

[6]  Rainer Waser,et al.  Degradation of dielectric ceramics , 1989 .

[7]  Shimeng Yu,et al.  Metal–Oxide RRAM , 2012, Proceedings of the IEEE.

[8]  Shimeng Yu,et al.  Improving Analog Switching in HfOx-Based Resistive Memory With a Thermal Enhanced Layer , 2017, IEEE Electron Device Letters.

[9]  Jongin Kim,et al.  Oxide based nanoscale analog synapse device for neural signal recognition system , 2015, 2015 IEEE International Electron Devices Meeting (IEDM).

[10]  D. Strukov,et al.  Nanoscale Resistive Switching in Amorphous Perovskite Oxide (a‐SrTiO3) Memristors , 2014 .

[11]  Bin Gao,et al.  Short Time High-Resistance State Instability of TaOx-Based RRAM Devices , 2017, IEEE Electron Device Letters.

[12]  Catherine D. Schuman,et al.  A Survey of Neuromorphic Computing and Neural Networks in Hardware , 2017, ArXiv.

[13]  R. Waser,et al.  Switching the electrical resistance of individual dislocations in single-crystalline SrTiO3 , 2006, Nature materials.

[14]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[15]  Cheol Seong Hwang,et al.  Short-term memory of TiO2-based electrochemical capacitors: empirical analysis with adoption of a sliding threshold , 2013, Nanotechnology.

[16]  E. Bienenstock,et al.  Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[17]  N. Wu,et al.  Evidence for an oxygen diffusion model for the electric pulse induced resistance change effect in transition-metal oxides. , 2006, Physical Review Letters.