Non-Volatile Memory and Synaptic Characteristics of TiN/CeOx/Pt RRAM Devices

Abstract

In this study, we investigate the synaptic characteristics and the non-volatile memory characteristics of TiN/CeOx/Pt RRAM devices for a neuromorphic system. The thickness and chemical properties of the CeOx are confirmed through TEM, EDS, and XPS analysis. A lot of oxygen vacancies (ions) in CeOx film enhance resistive switching. The stable bipolar resistive switching characteristics, endurance cycling (>100 cycles), and non-volatile properties in the retention test (>10,000 s) are assessed through DC sweep. The filamentary switching model and Schottky emission-based conduction model are presented for TiN/CeOx/Pt RRAM devices in the LRS and HRS. The compliance current (1~5 mA) and reset stop voltage (−1.3~−2.2 V) are used in the set and reset processes, respectively, to implement multi-level cell (MLC) in DC sweep mode. Based on neural activity, a neuromorphic system is performed by electrical stimulation. Accordingly, the pulse responses achieve longer endurance cycling (>10,000 cycles), MLC (potentiation and depression), spike-timing dependent plasticity (STDP), and excitatory postsynaptic current (EPSC) to mimic synapse using TiN/CeOx/Pt RRAM devices.

Low-Frequency Noise-Based Mechanism Analysis of Endurance Degradation in Al/αTiOx/Al Resistive Random Access Memory Devices

“…In recent decades, resistive switching random access memory (RRAM) has received much attention as an emerging non-volatile memory (NVM) technology because of CMOS-compatible materials, simple cell structure, good scalability (<10 nm), low switching current (~nA), and 3D integration [ 8 , 9 , 10 ]. In addition, recently, in advanced computing technologies for neuromorphic systems, RRAM has also attracted great attention as one of the promising candidates for synaptic electronics for the hardware implementation of artificial neural networks owing to its non-volatility, repeatable analog switching with good precision, and the potential for large-scale integration with the crossbar array structure [ 11 , 12 , 13 ]. Although great progress has been made recently, research on RRAM still faces some significant challenges, such as the broad distribution of the switching parameters (V forming , V set , V reset , R HRS , and R LRS ), retention failure, and endurance degradation [ 14 , 15 , 16 ].…”

TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing: from synaptic plasticity to stochastic resonance

“…Hence, the role of resistive memories in conventional neural networks consists in implementing the synaptic weights. These weights are obtained by means of a quantization process, employing a multilevel conductance approach for the memristive device operation (Milo et al, 2016 ; Perez et al, 2017 ; González-Cordero et al, 2019 ; Sokolov et al, 2019 ; Ren et al, 2020 ; Ha et al, 2022 ; Roldán et al, 2023a ).…”