READ ME File For 'Dataset for Back-End-of-Line SiC-Based Memristor for Resistive Memory and Artificial Synapse'

Dataset DOI: 10.5258/SOTON/D2165

ReadMe Author: Ruomeng Huang, University of Southampton 

This dataset supports the publication: Back-End-of-Line SiC-Based Memristor for Resistive Memory and Artificial Synapse
AUTHORS: Omesh Kapur, Dongkai Guo, Yisong Han, Richard Beanland, Liudi Jiang, C. H. de Groot and Ruomeng Huang
TITLE: Back-End-of-Line SiC-Based Memristor for Resistive Memory and Artificial Synapse
JOURNAL: Advanced Electronic Materials
PAPER DOI IF KNOWN: 10.1002/aelm.202200312


This dataset contains:
The raw data of figure 1 to 8.

The figures are as follows:

Figure 1. Characterization of the SiC film and Cu/SiC/W memristor. a) Schematic of the Cu/SiC/W memristor structure. b) Cross-sectional TEM image
of the memristor. c) XPS depth profile characterization of the SiC film on W electrode. d) XPS core-level spectra of Si2p and C1s of the SiC film.
Figure 2. Electrical characterization of the SiC memristor. a) The DC-IV curves of the electro-forming and subsequent switching cycles; inset is the
subsequent cycle plotting in linear scale showing a high level of self-rectification. b) Representative DC-IV cycles from six different devices. c) 100 DC-IV
cycles of a device. d) DC endurance and e) the resistance states distribution of the device over 100 switching cycles. f) Retention of the SiC memristor.
Figure 3. I-V characteristic of the SiC memristor with resistance state modulation via DC stimulation. Current as a function of continuous DC sweep
with a) increasing positive voltage and b) constant voltage of 5 V. c) The gradual modulation of the memristor conductance via the different number
of DC stimulation and positive voltages; Current as a function of continuous DC sweep with d) increasing negative voltage and e) constant voltage of
−3 V. f) The gradual modulation of the memristor conductance via a different number of DC stimulation and negative voltages. Insets show current
and conductance at 0.1 V.
Figure 4. Synaptic behaviors of the SiC based artificial synapse under electric field regulation. a) Schematic representation of a biological neural
network and a memristor device showing the correspondence between biological and electronic synapses. b) Gradual PSC change with a series of
voltage pulses (+5 V) and the subsequent auto-decay showing the STP behavior. c) Current response of the device for pulses with different inter-spike
intervals, emulating SRDP behavior. d) Mean changes in the current during the application of 10 pulses for pulse trains with different pulse intervals.
The rectangular boxes indicate PPF and PTP behavior.
Figure 5. Experimental demonstration of the essential biological synaptic functions for neuromorphic computing. a) Current response of the device
for pulses with different pulse amplitude, emulating SVDP behavior. b) Mean changes in the current during the application of 10 pulses for pulse trains
with different pulse amplitudes. c) Current response of the device for pulses with different pulse duration, emulating SDDP behavior. d) Mean changes
in the current during the application of 10 pulses for pulse trains with different pulse durations.
Figure 6. Memory retention data recorded (dots) after a) different numbers of identical stimuli, and b) different pulse intervals with fitted curves using
the SEF (solid lines). The data are scaled by a prefactor M0. Characteristic relaxation time (τ) was obtained as a function of c) the numbers of identical
stimuli, and d) pulse interval.
Figure 7. The “learning-forgetting-rehearsal” process of 8 cycles.
Figure 8. Emulation of “learning-forgetting-rehearsal” memory function in a 3 × 3 array. The current response image mapping of the SiC artificial
synapse after a) 1, b) 4, and c) 10 learning and rehearsal cycles at different times.


Date of data collection: October 2019 to April 2022

Information about geographic location of data collection: United Kingdom

Licence: No

Related projects: EPSRC and AWE Ltd. for the ICASE studentship No. 16000087


Date that the file was created: June, 2022