Biomass‐Derived Nanoporous Graphene Memory Cell

Sattari-Esfahlan, S. M.; Bonnassieux, Yvan; Kymissis, Ioannis; Kim, Chang‐Hyun

doi:10.1002/admi.202200084

Cited by 4 publications

(5 citation statements)

References 64 publications

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“…This might be due to the lower V th value observed for lower channel length devices. This is in agreement with our previously reported results on NPG device [22], where we observed that the value of V th for 80 µm and 60 µm devices are lower in comparison to the 100 µm channel length device. As discussed before, the threshold switching in NPG devices arises due to the accumulation of interfacial oxygen ions.…”

Section: Spike Frequency Optimizationsupporting

confidence: 94%

“…1(b). The threshold switching behavior of the device is clear from both the figures where the device switches from a high resistance state (HRS) to a low resistance state (LRS) at a threshold voltage (V th ) of 4.9 V and a reverse switching from LRS to HRS at a hold voltage (V hold ) of 0.1 V. Such a volatile switching behavior can be attributed to the oxygen ion accumulation at the interface between the anode and NPG channel [22]. Interestingly, the V hold is very close to 0 V and hence the LRS region spans a wider voltage range (∼4.8 V) which is an added advantage of the NPG device and not seen in previous reports based on 2D material based memristors [20].…”

Section: A Device Descriptionmentioning

confidence: 95%

“…The NPG graphene-based device was fabricated from sugar cane bagasse (Saccharum officinarum) using a dry transfer technique. The details of the fabrication procedure has been reported in our previous publication [22]. The NPG device has a lateral structure with Au/NPG/Au configuration deposited over an insulating substrate with a channel length of 100 µm.…”

Section: A Device Descriptionmentioning

confidence: 99%

“…(c) Endurance characteristics on the NPG device showing stable ON and OFF states for 200 switching cycles. Adapted from supplementary data of[22]…”

mentioning

confidence: 99%

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Optimization of Leaky Integrate-and-Fire Neuron Circuits Based on Nanoporous Graphene Memristors

Mohanan,

Sattari-Esfahlan,

Cho

et al. 2024

IEEE J. Electron Devices Soc.

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Artificial neurons form the core of neuromorphic computing which is emerging as an alternative for the von Neumann computing architecture. However, existing neuron architectures still lack in area efficiency, especially considering the huge size of modern neural networks requiring millions of neurons. Here, we report on a compact leaky integrate and fire (LIF) neuron circuit based on graphene memristor device. The LIF circuit exhibits various biological properties like threshold control, leaky integration and reset behavior. Circuit parameters like the synaptic resistance and membrane capacitance act as additional control parameters whereby the spike frequency of the circuit can be effectively controlled. Uniquely, the circuit exhibits biologically realistic frequencies as low as 286 Hz. The results suggest the suitability of this compact and biorealistic LIF neuron circuit towards future bioinspired computing systems

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Section: Spike Frequency Optimizationsupporting

confidence: 94%

Section: A Device Descriptionmentioning

confidence: 95%

Section: A Device Descriptionmentioning

confidence: 99%

“…(c) Endurance characteristics on the NPG device showing stable ON and OFF states for 200 switching cycles. Adapted from supplementary data of[22]…”

mentioning

confidence: 99%

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Optimization of Leaky Integrate-and-Fire Neuron Circuits Based on Nanoporous Graphene Memristors

Mohanan,

Sattari-Esfahlan,

Cho

et al. 2024

IEEE J. Electron Devices Soc.

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“…Such adjustable electronic features are highly suited for the emulation of neurological responses. Graphene [ 145 ] has been the most widely studied 2D material, mainly due to its unique electronic and mechanical properties. H. Tian et al [ 146 ] reported the first graphene-based synaptic device having a standard FET configuration and an additional bottom gate electrode.…”

Section: Resistive Switching Materials and Applicationsmentioning

confidence: 99%

Resistive Switching Devices for Neuromorphic Computing: From Foundations to Chip Level Innovations

Udaya Mohanan

2024

Nanomaterials

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Neuromorphic computing has emerged as an alternative computing paradigm to address the increasing computing needs for data-intensive applications. In this context, resistive random access memory (RRAM) devices have garnered immense interest among the neuromorphic research community due to their capability to emulate intricate neuronal behaviors. RRAM devices excel in terms of their compact size, fast switching capabilities, high ON/OFF ratio, and low energy consumption, among other advantages. This review focuses on the multifaceted aspects of RRAM devices and their application to brain-inspired computing. The review begins with a brief overview of the essential biological concepts that inspire the development of bio-mimetic computing architectures. It then discusses the various types of resistive switching behaviors observed in RRAM devices and the detailed physical mechanisms underlying their operation. Next, a comprehensive discussion on the diverse material choices adapted in recent literature has been carried out, with special emphasis on the benchmark results from recent research literature. Further, the review provides a holistic analysis of the emerging trends in neuromorphic applications, highlighting the state-of-the-art results utilizing RRAM devices. Commercial chip-level applications are given special emphasis in identifying some of the salient research results. Finally, the current challenges and future outlook of RRAM-based devices for neuromorphic research have been summarized. Thus, this review provides valuable understanding along with critical insights and up-to-date information on the latest findings from the field of resistive switching devices towards brain-inspired computing.

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A new and tunable method for the NDR engineering of nanoribbon materials and devices

Niyat

Zare

Hosseini

2023

Materials Science and Engineering: B

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Biomass‐Derived Nanoporous Graphene Memory Cell

Abstract: The data that support the findings of this study are available from the corresponding author upon reasonable request.

Cited by 4 publications

References 64 publications

Optimization of Leaky Integrate-and-Fire Neuron Circuits Based on Nanoporous Graphene Memristors

Optimization of Leaky Integrate-and-Fire Neuron Circuits Based on Nanoporous Graphene Memristors

Resistive Switching Devices for Neuromorphic Computing: From Foundations to Chip Level Innovations

A new and tunable method for the NDR engineering of nanoribbon materials and devices

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