Ultra-Low-Power Hybrid Synaptic Memristor via Simultaneous-Controlled Mechanism of Cations and Anions for Hyper-Scale Synaptic Cores
Authors: Woo Dae-Seong , Kim Jae Kyeong , Shim Tae-Hun , Park Jinsub , Park Jea-Gun 
Published: August 2024

Recently, memristive devices such as phase change random access memory (PCRAM), resistive random access memory (ReRAM), and ferroelectric random access memory (FeRAM) have been actively researched to implement hyper-scale synaptic cores of various ANNs, i.e., deep neural networks (DNNs), spiking neural networks (SNNs), convolutional neural networks (CNNs), and binarized neural networks (BNNs). In particular, ReRAM is currently being highlighted as an artificial synaptic device because of its multilevel capability (> 11 bits)1, high switching speed (< 100 ps)2, high endurance (> 1012 cycles)3, high scalability, and complementary metal-oxide-semiconductor (CMOS) compatibility4. ReRAM is generally divided into two types: valence change memory (VCM) cells having oxygen vacancy filaments and electrochemical metallization (ECM) cells having conductive metal filaments. VCM cells have great retention but exhibit high switching current (~ 100 μA). In contrast, ECM cells exhibit relatively lower switching current (~10 uA) than VCM cells but have poor retention due to diffusion phenomena at high external temperatures5. Due to their high switching current and poor reliability, VCM and ECM cells have limitations in their application as synaptic devices in next-generation neuromorphic systems for energy consumption similar to that of the human brain (~20 W).

In this study, for the first time, we developed a Ru-based ultra-low-power (< 1μA) hybrid synaptic memristor having a simultaneous-controlled mechanism of Ru cations and oxygen anions in a resistive switching layer. To elucidate the simultaneous-controlled mechanism of Ru cations and oxygen anions, we fabricated the VCM, ECM, and Ru-based hybrid memristor and performed x-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS) analysis depending on the resistance states (i.e., pristine, set, and reset). In addition, the mobility of mobile species in VCM, ECM, and Ru-based hybrid memristors was calculated indirectly via electrical properties (switching speed, operating voltage) and ToF-SIMS depth profiles, revealing the mechanism by which Ru-based hybrid memristor has low switching currents (< 1μA). Finally, the power consumption of active synaptic cores in the training and inference process of the designed DNN was evaluated for VCM, ECM, and Ru-based hybrid memristor. The simultaneous-controlled mechanism of Ru cations and oxygen anions of the Ru-based hybrid memristor and its application to DNN will be presented in detail.

Acknowledgement

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. RS-2023-00260527) and Institute of Information & communications Technology Planning & Evaluation (IITP) under the artificial intelligence semiconductor support program to nurture the best talents (IITP-(2023)-RS-2023-00253914) grant funded by the Korea government(MSIT)".

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