Control of the Boundary between the Gradual and Abrupt Modulation of Resistance in the Schottky Barrier Tunneling-Modulated Amorphous Indium-Gallium-Zinc-Oxide Memristors for Neuromorphic Computing

The transport and synaptic characteristics of the two-terminal Au/Ti/ amorphous Indium-Gallium-Zinc-Oxide (a-IGZO)/thin SiO2/p+-Si memristors based on the modulation of the Schottky barrier (SB) between the resistive switching (RS) oxide layer and the metal electrodes are investigated by modulating the oxygen content in the a-IGZO film with the emphasis on the mechanism that determines the boundary of the abrupt/gradual RS. It is found that a bimodal distribution of the effective SB height (ΦB) results from further reducing the top electrode voltage (VTE)-dependent Fermi-level (EF) followed by the generation of ionized oxygen vacancies (VO2+s). Based on the proposed model, the influences of the readout voltage, the oxygen content, the number of consecutive VTE sweeps on ΦB, and the memristor current are explained. In particular, the process of VO2+ generation followed by the ΦB lowering is gradual because increasing the VTE-dependent EF lowering followed by the VO2+ generation is self-limited by increasing the electron concentration-dependent EF heightening. Furthermore, we propose three operation regimes: the readout, the potentiation in gradual RS, and the abrupt RS. Our results prove that the Au/Ti/a-IGZO/SiO2/p+-Si memristors are promising for the monolithic integration of neuromorphic computing systems because the boundary between the gradual and abrupt RS can be controlled by modulating the SiO2 thickness and IGZO work function.

[1]  Timothée Masquelier,et al.  STDP-based spiking deep neural networks for object recognition , 2016, Neural Networks.

[2]  Yanhao Du,et al.  Emerging Artificial Synaptic Devices for Neuromorphic Computing , 2019, Advanced Materials Technologies.

[3]  L. Kish.  End of Moore's law: thermal (noise) death of integration in micro and nano electronics , 2002 .

[4]  Pritish Narayanan,et al.  Neuromorphic computing using non-volatile memory , 2017 .

[5]  Neuro-Inspired Computing With Emerging Nonvolatile Memory , 2018 .

[6]  Sung-Jin Choi,et al.  Effect of direct current sputtering power on the behavior of amorphous indium-gallium- zinc-oxide thin-film transistors under negative bias illumination stress: A combination of experimental analyses and device simulation , 2015 .

[7]  Y. Uraoka,et al.  Thermal analysis for observing conductive filaments in amorphous InGaZnO thin film resistive switching memory , 2014 .

[8]  A. Janotti,et al.  Native point defects in ZnO , 2007 .

[9]  Shimeng Yu,et al.  Neuro-Inspired Computing With Emerging Nonvolatile Memorys , 2018, Proceedings of the IEEE.

[10]  Sung-Jin Choi,et al.  Study on the photoresponse of amorphous In-Ga-Zn-O and zinc oxynitride semiconductor devices by the extraction of sub-gap-state distribution and device simulation. , 2015, ACS applied materials & interfaces.

[11]  Shimeng Yu,et al.  Ferroelectric FET analog synapse for acceleration of deep neural network training , 2017, 2017 IEEE International Electron Devices Meeting (IEDM).

[12]  Jin Jang,et al.  Light/negative bias stress instabilities in indium gallium zinc oxide thin film transistors explained by creation of a double donor , 2012 .

[13]  John von Neumann,et al.  First draft of a report on the EDVAC , 1993, IEEE Annals of the History of Computing.

[14]  Giacomo Indiveri,et al.  Memory and Information Processing in Neuromorphic Systems , 2015, Proceedings of the IEEE.

[15]  J. Yang,et al.  Emerging Memory Devices for Neuromorphic Computing , 2019, Advanced Materials Technologies.

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

[17]  Wei Hu,et al.  Highly uniform resistive switching properties of amorphous InGaZnO thin films prepared by a low temperature photochemical solution deposition method. , 2014, ACS applied materials & interfaces.

[18]  Kinam Kim,et al.  Gated three-terminal device architecture to eliminate persistent photoconductivity in oxide semiconductor photosensor arrays. , 2012, Nature materials.

[19]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[20]  John F. Wager,et al.  Negative bias illumination stress assessment of indium gallium zinc oxide thin‐film transistors , 2015 .

[21]  H. Kim,et al.  RRAM-based synapse for neuromorphic system with pattern recognition function , 2012, 2012 International Electron Devices Meeting.

[22]  Sungho Kim,et al.  Pattern Recognition Using Carbon Nanotube Synaptic Transistors with an Adjustable Weight Update Protocol. , 2017, ACS nano.

[23]  Ru Huang,et al.  Stochastic neuron based on IGZO Schottky diodes for neuromorphic computing , 2019, APL Materials.

[24]  Yichun Liu,et al.  Effect of reset voltage polarity on the resistive switching region of unipolar memory , 2015 .

[25]  Y. Liu,et al.  Synaptic Learning and Memory Functions Achieved Using Oxygen Ion Migration/Diffusion in an Amorphous InGaZnO Memristor , 2012 .

[26]  Shimeng Yu,et al.  A neuromorphic visual system using RRAM synaptic devices with Sub-pJ energy and tolerance to variability: Experimental characterization and large-scale modeling , 2012, 2012 International Electron Devices Meeting.

[27]  C. Zhang,et al.  Programmable Electrofluidics for Ionic Liquid Based Neuromorphic Platform , 2019, Micromachines.