Dynamic and Static Switching in ITO/SnOx/ITO and Its Synaptic Application

Abstract

The attempts to devise networks that resemble human minds are steadily progressing through the development and diversification of neural networks (NN), such as artificial NN (ANN), convolution NN (CNN), and recurrent NN (RNN). Meanwhile, memory devices applied on the networks are also being studied together, and RRAM is the one of the most promising candidates. The fabricated ITO/SnOX/TaN device showed two forms of current–voltage (I-V) curves, classified as dynamic and static. It was triggered from the forming process, and the difference between the two curves resulted from the data retention measured at room temperature for 103 s. The dynamic curve shows a time-dependent change in the data, and the cause of the data preservation period was considered through X-ray photoelectron spectroscopy (XPS) and linear fitting in conduction mechanisms. To confirm whether the memory performance of the device may be implemented on the synapse, the change in the plasticity was confirmed using a rectangular-shaped pulse. Paired-pulse facilitation (PPF) was implemented, and the change from short-term potentiation (STP) to long-term potentiation (LTP) was achieved.

Double-Forming Mechanism of TaOx-Based Resistive Memory Device and Its Synaptic Applications

“…By contrast, for ∆t < 0, the weight change decreased, and LTD was obtained. This result proves the successful experimental demonstration of the STDP learning rule with synaptic weight changes at different spike times using the proposed Pt/TaO x /InO x /ITO memristor device, favorably mimicking a biological synapse [23]. Additionally, another Hebbian learning rule, SRDP, was tested to obtain the device's frequency-dependent characteristics [64].…”

“…Among these, RRAM devices have benefits such as simple fabrication, high switching speeds, outstanding scalability, and high endurance, making them one of the most promising choices [ 19 , 20 , 21 , 22 ]. Moreover, the simple two-terminal structure of RRAMs, comprising a switching layer sandwiched between the top and bottom electrodes, most closely emulates the structure of a biological synapse [ 23 ]. Furthermore, applying different biases with different polarities causes a phenomenon termed the electro-resistance effect, where the resistance condition changes between a low-resistance state (LRS) and a high-resistance state (HRS), and which information is stored at 0 s and 1 s, respectively [ 24 , 25 ].…”

Artificial Synapse Emulated by Indium Tin Oxide/SiN/TaN Resistive Switching Device for Neuromorphic System

“…Additionally, charge-based NAND flash memory is about to face limitations in nonvolatile storage technology due to its scaling limits [5]. To address the aforementioned issues, different next-generation non-volatile memories such as phase-change memory (PCM) [6], magnetic random-access memory (MRAM) [7], ferroelectric random-access memory (FRAM) [8], and resistive random-access memory (RRAM) [2,[9][10][11][12][13] are emerging. RRAM is a viable contender among them due to its high scalability, low-power operation, fast switching speed, long retention time, and high endurance [14][15][16][17][18][19][20][21].…”