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. 2024 Sep 23;9(9):578.
doi: 10.3390/biomimetics9090578.

Oxygen-Plasma-Treated Al/TaOX/Al Resistive Memory for Enhanced Synaptic Characteristics

Gyeongpyo Kim  1 , Seoyoung Park  1 , Minsuk Koo  2 , Sungjun Kim  1
Affiliations

Oxygen-Plasma-Treated Al/TaOX/Al Resistive Memory for Enhanced Synaptic Characteristics

Gyeongpyo Kim et al. Biomimetics (Basel). .

Abstract

In this study, we investigate the impact of O2 plasma treatment on the performance of Al/TaOX/Al-based resistive random-access memory (RRAM) devices, focusing on applications in neuromorphic systems. Comparative analysis using scanning electron microscopy and X-ray photoelectron spectroscopy confirmed the differences in chemical composition between O2-plasma-treated and untreated RRAM cells. Direct-current measurements showed that O2-plasma-treated RRAM cells exhibited significant improvements over untreated RRAM cells, including higher on/off ratios, improved uniformity and distribution, longer retention times, and enhanced durability. The conduction mechanism is investigated by current-voltage (I-V) curve fitting. In addition, paired-pulse facilitation (PPF) is observed using partial short-term memory. Furthermore, 3- and 4-bit weight tuning with auto-pulse-tuning algorithms was achieved to improve the controllability of the synapse weight for the neuromorphic system, maintaining retention times exceeding 103 s in the multiple states. Neuromorphic simulation with an MNIST dataset is conducted to evaluate the synaptic device.

Keywords: artificial synapse; neuromorphic system; plasma treatment; resistive switching.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Schematic diagram of the two fabricated Al/TaOX/Al devices: (a) without OPT and (b) with OPT. Images of the top electrodes for (c) Al/TaOX/Al and (d) Al/TaOX (OPT)/TaOX (OPT) structures. (e) Current injection diagram. (f) Actual measurement image. (g) SEM image of an Al/TaOX/Al device. XPS spectra of Ta 4f on the (h) TaOX layer and (i) TaOX layer with OPT.
Figure 2
Figure 2
I–V characteristics of each RRAM device for 30 cycles: (a) Al/TaOX/Al device and (b) Al/TaOX (OPT)/Al (OPT) device. (c) Distribution characteristics of HRS/LRS for 50 cells on each device. (d) Distribution characteristics of the set/reset voltage for the Al/TaOX/Al device. (e) Distribution characteristics of the reset voltage for the Al/TaOX (OPT)/Al (OPT) device.
Figure 3
Figure 3
Endurance of the (a) Al/TaOX/Al device and (b) Al/TaOX (OPT)/Al (OPT) device measured by repetitive DC voltage application. Retention measurement for the (c) Al/TaOX/Al device and (d) Al/TaOX (OPT)/Al (OPT) device at a constant voltage of 0.1 V.
Figure 4
Figure 4
Schematic diagram of the switching mechanism. The Al/TaOX/Al device is in the (a) pristine state, (b) with a positive voltage, (c) LRS, and (d) with a negative voltage. The pristine state of the Al/TaOX (OPT)/Al (OPT) device. Continuous application of a positive voltage. The Al/TaOX (OPT)/Al (OPT) device in the (e) pristine state, (f) with a positive voltage, (g) LRS, and (h) with a negative voltage. (i) Typical I–V curve of the OPT. (j) ln(I) versus V1/2 plot for the Schottky emission mechanism (red). (k) Linear I–V plot in the log–log scale for ohmic conduction (blue).
Figure 5
Figure 5
(a) Biological system and schematic diagram of a memristor device system used as an artificial synapse. (b) Natural decay process involving a set pulse in the interval times between the pulses. The trend line (Rapid natural decay) and solid line of the decay (saturation). (c) Statistical distribution of PPF as a function of the interval time. (Experiment data and Fitting curve).
Figure 6
Figure 6
Auto-conductance-tuning method for the MLC implementation, with box charts used to represent each level of the (a) 3-bit MLC (G1 ranges from 5 μs to 8.3 μs, and G8 ranges from 28.3 μs to 31.6 μs) and (b) 4-bit MLC (G1 ranges from 5 μs to 8.3 μs, and G16 ranges from 51.7 μs to 55 μs). (c) MNIST classification accuracy. (d) Multilevel retention measurement with different CCs (1 mA (grey), 3 mA (red), 5 mA (blue), 7 mA (yellow), 10 mA (green)). Retention test for up to 103 s in multiple states. (e) Recognition accuracy of the trained retention system on the MNIST dataset.
Figure 7
Figure 7
(a) Concept of reservoir computing. (b) Sixteen states as per specific pulse streams. (c) Reservoir computing system with a pulse train, RRAM devices, and output node. (d) Digit “3” implemented using 5 × 4 pixels. (e) Conceptual diagram of reservoir computing with pixel binarization. (f) Training of the readout layer using a CNN on the MNIST dataset. (g) Recognition accuracy of the trained reservoir computing system on the MNIST dataset.
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References

    1. Zahoor F., Azni Zulkifli T.Z., Khanday F.A. Resistive Random Access Memory (RRAM): An Overview of Materials, Switching Mechanism, Performance, Multilevel Cell (Mlc) Storage, Modeling, and Applications. Nanoscale Res. Lett. 2020;15:90. doi: 10.1186/s11671-020-03299-9. - DOI - PMC - PubMed
    1. Ahn J.H., Choi H.S., Kim J.N., Park B.G., Kim S., Lee J., Kim Y. On-Chip Adaptive Matching Learning with Charge-Trap Synapse Device and ReLU Activation Circuit. Solid State Electron. 2021;186:108177. doi: 10.1016/j.sse.2021.108177. - DOI
    1. Yang R., Huang H.M., Guo X. Memristive Synapses and Neurons for Bioinspired Computing. Adv. Electron. Mater. 2019;5:1900287. doi: 10.1002/aelm.201900287. - DOI
    1. Yang J.J., Strukov D.B., Stewart D.R. Memristive Devices for Computing. Nat. Nanotechnol. 2013;8:13–24. doi: 10.1038/nnano.2012.240. - DOI - PubMed
    1. Kang D., Jang J.T., Park S., Ansari M.H.R., Bae J.H., Choi S.J., Kim D.M., Kim C., Cho S., Kim D.H. Threshold-Variation-Tolerant Coupling-Gate α-IGZO Synaptic Transistor for More Reliably Controllable Hardware Neuromorphic System. IEEE Access. 2021;9:59345–59352. doi: 10.1109/ACCESS.2021.3072688. - DOI

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