Abstract
In this work, we propose three types of resistive switching behaviors by controlling operation conditions. We confirmed well-known filamentary switching in Al2O3-based resistive switching memory using the conventional device working operation with a forming process. Here, filamentary switching can be classified into two types depending on the compliance current. On top of that, the homogeneous switching is obtained by using a negative differential resistance effect before the forming or setting process in a negative bias. The variations of the low-resistance and high-resistance states in the homogeneous switching are comparable to the filamentary switching cases. However, the drift characteristics of the low-resistance and high-resistance states in the homogeneous switching are unstable with time. Therefore, the short-term plasticity effects, such as the current decay in repeated pulses and paired pulses facilitation, are demonstrated when using the resistance drift characteristics. Finally, the conductance can be increased and decreased by 50 consecutive potentiation pulses and 50 consecutive depression pulses, respectively. The linear conductance update in homogeneous switching is achieved compared to the filamentary switching, which ensures the high pattern-recognition accuracy.
Highlights
Data usage required by new technologies, such as self-driving cars, Internet of Things (IoT), and Artificial intelligence (AI), has been increasing rapidly [1]
One solution was found in the human brain, in which more than 100 billion neurons communicate with other neurons through 100 trillion synapses, processing and storing information in an instant
We closely investigated the homogeneous resistive switching in the trapping/detrapping region without the electroforming as well as the conventional filamentary switching operation in Al2O3 in terms of the variation and retention of high-resistance state (HRS) and low-resistance state (LRS), multi-level properties, and synapse emulation properties
Summary
Data usage required by new technologies, such as self-driving cars, Internet of Things (IoT), and Artificial intelligence (AI), has been increasing rapidly [1]. Resistive switching random access memory (RRAM) can act as a synapse in a neuromorphic chip because of its low-power operation [7], fast switching time [8], high-density integration [9], and multi-level cells (MLC) with analogue switching [10,11,12,13,14]. Studies on the homogeneous switching properties for neuromorphic computers in Al2O3-based RRAM devices have been few. We closely investigated the homogeneous resistive switching in the trapping/detrapping region without the electroforming as well as the conventional filamentary switching operation in Al2O3 in terms of the variation and retention of high-resistance state (HRS) and low-resistance state (LRS), multi-level properties, and synapse emulation properties
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