Synaptic behaviors of HfO2 ReRAM by pulse frequency modulation

Lee, Jaewoong; Kim, Min-Hwi; Kim, Tae-Hyeon; Bang, Suhyun; Choi, Yeon-Joon; Kim, Sungjun; Cho, Seongjae

doi:10.1016/j.sse.2019.02.008

Cited by 11 publications

(7 citation statements)

References 21 publications

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“…Compared with conventional silicon-based memory devices like flash memory, it is noted that RRAM devices have demonstrated a series of advantages such as low operation voltage, low power consumption, high density, and enhanced compatibility with traditional complementary metal oxide semiconductor (CMOS) technology [31][32][33]. In addition, with the deepening of research on artificial intelligence (AI) hardware equipment, biomimetic synapse behaviors of RRAM devices have also received extensive attention, which has non-negligible influence in the investigation of electrical artificial synapse [15,17,[34][35][36][37][38]. However, some other limitations and challenges of RRAM devices cannot be neglected, such as synthesis methods of RS materials, stability of device performance and storage mechanism of devices with different materials.…”

Section: Biological (Silk Protein) Polymer (Pvk) Perovskite (Ch3nh3snmentioning

confidence: 99%

“…However, conventional synaptic devices cannot meet the requirements of ANNs because of some technological limitations like large device area, high power consumption and slow response speed. As illustrated in Figure 10 b, emerging synaptic device can be integrated in to a single unit and improves the processing efficiency and processing accuracy [ 15 , 17 , 34 , 35 , 36 , 37 , 38 ]. The superior RS characteristics demonstrated in RRAM devices reveal its great potential in the neuromorphic application based on ANNs.…”

Section: Bionic Synaptic Applicationmentioning

confidence: 99%

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Advances of RRAM Devices: Resistive Switching Mechanisms, Materials and Bionic Synaptic Application

et al. 2020

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Resistive random access memory (RRAM) devices are receiving increasing extensive attention due to their enhanced properties such as fast operation speed, simple device structure, low power consumption, good scalability potential and so on, and are currently considered to be one of the next-generation alternatives to traditional memory. In this review, an overview of RRAM devices is demonstrated in terms of thin film materials investigation on electrode and function layer, switching mechanisms and artificial intelligence applications. Compared with the well-developed application of inorganic thin film materials (oxides, solid electrolyte and two-dimensional (2D) materials) in RRAM devices, organic thin film materials (biological and polymer materials) application is considered to be the candidate with significant potential. The performance of RRAM devices is closely related to the investigation of switching mechanisms in this review, including thermal-chemical mechanism (TCM), valance change mechanism (VCM) and electrochemical metallization (ECM). Finally, the bionic synaptic application of RRAM devices is under intensive consideration, its main characteristics such as potentiation/depression response, short-/long-term plasticity (STP/LTP), transition from short-term memory to long-term memory (STM to LTM) and spike-time-dependent plasticity (STDP) reveal the great potential of RRAM devices in the field of neuromorphic application.

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Section: Biological (Silk Protein) Polymer (Pvk) Perovskite (Ch3nh3snmentioning

confidence: 99%

Section: Bionic Synaptic Applicationmentioning

confidence: 99%

Advances of RRAM Devices: Resistive Switching Mechanisms, Materials and Bionic Synaptic Application

et al. 2020

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“…Feali et al [864] studied the reliability of spike timing for spike train generated by memristors in which probabilistic and noise features were included. However, where the density of neurons is not as high and the stimulus tends to be slowly varying, a frequency code is a more robust alternative [865] , [866] , [867] , [652] . Feali et al [868] also emulated neuristor adaptive behavior (spike-frequency adaptation) in SPICE environment.…”

Section: Neural Codesmentioning

confidence: 99%

Functional Oxides for Photoneuromorphic Engineering: Toward a Solar Brain

Pérez‐Tomás

2019

Adv Materials Inter

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New device concepts and new computing principles are needed to balance our ever-growing appetite for data and information with the realization of the goals of increased energy efficiency, reduction in CO 2 emissions and the circular economy. Neuromorphic or synaptic electronics is an emerging field of research aiming to overcome the current computer's Von-Neumann bottleneck by building artificial neuronal systems to mimic the extremely energy efficient biological synapses. The introduction of photovoltaic and/or photonic aspects into these neuromorphic architectures will produce self-powered adaptive electronics but may also open up new possibilities in artificial neuroscience, artificial neural communications, sensing and machine learning which would enable, in turn, a new era for computational systems owing to the possibility of attaining high bandwidths with much reduced power consumption. This perspective is focused on recent progress in the implementation of functional oxide thinfilms into photovoltaic and neuromorphic applications towards the envisioned goal of selfpowered photovoltaic neuromorphic systems or a solar brain.

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“…This consumption is much smaller than that of a conventional computing system, which consumes approximately 56 kW per hour [ 5 , 6 , 7 , 8 ]. A neuromorphic system can emulate biological synapses on a hardware level, with the aim of a low power consumption, fault tolerance, and high efficiency processing [ 9 , 10 , 11 ]. By structuring integrated circuits in the form of artificial neural networks, it is possible to process data for each neural network.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Memory Dynamics of TiN/Ti/TiO2/SiOx/Si Resistive Random Access Memory

Cho

Kim

2020

Nanomaterials

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In this study, we investigated the synaptic functions of TiN/Ti/TiO2/SiOx/Si resistive random access memory for a neuromorphic computing system that can act as a substitute for the von-Neumann computing architecture. To process the data efficiently, it is necessary to coordinate the information that needs to be processed with short-term memory. In neural networks, short-term memory can play the role of retaining the response on temporary spikes for information filtering. In this study, the proposed complementary metal-oxide-semiconductor (CMOS)-compatible synaptic device mimics the potentiation and depression with varying pulse conditions similar to biological synapses in the nervous system. Short-term memory dynamics are demonstrated through pulse modulation at a set pulse voltage of −3.5 V and pulse width of 10 ms and paired-pulsed facilitation. Moreover, spike-timing-dependent plasticity with the change in synaptic weight is performed by the time difference between the pre- and postsynaptic neurons. The SiOx layer as a tunnel barrier on a Si substrate provides highly nonlinear current-voltage (I–V) characteristics in a low-resistance state, which is suitable for high-density synapse arrays. The results herein presented confirm the viability of implementing a CMOS-compatible neuromorphic chip.

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Synaptic behaviors of HfO2 ReRAM by pulse frequency modulation

Cited by 11 publications

References 21 publications

Advances of RRAM Devices: Resistive Switching Mechanisms, Materials and Bionic Synaptic Application

Advances of RRAM Devices: Resistive Switching Mechanisms, Materials and Bionic Synaptic Application

Functional Oxides for Photoneuromorphic Engineering: Toward a Solar Brain

Short-Term Memory Dynamics of TiN/Ti/TiO2/SiOx/Si Resistive Random Access Memory

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