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Prakash, Gnana

doi:10.26452/fjphs.v1i1.143

Cited by 3 publications

(6 citation statements)

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“…Literature indicates that multiple resistive states in RRAM devices can be realized through methods like modulating current compliance during the set process, [28,39,40] adjusting the reset stop voltage in the reset process, [28,32,41] using light illumination, [39,42] and tweaking pulse width. [5,27] In many studies, the multilevel resistance states in lead-free perovskite memory devices have been manipulated by adjusting the current compliance during the set process. [31,[43][44][45][46][47][48] In our study, multiple HRS states (R2-R6) were achieved by incrementing the reset stop voltage from 2 to 6 V at intervals of 1 V (Figure 2g).…”

Section: Rram Applicationmentioning

confidence: 99%

“…[ 4 ] However, these devices also face challenges related to variability, data retention, write disturbances, and multilevel cell stability, to list a few. [ 5 ] In this sense, the pursuit of RRAM materials that promise higher performance, better energy efficiency, and novel functionalities remains incessant. [ 6 ] Accordingly, an emerging class of materials such as perovskites has attracted intense interest given their potential, such as higher On/Off ratios, reduced switching voltages, unique optical multiple functionalities, and compatibility with novel flexible form factors, among others.…”

Section: Introductionmentioning

confidence: 99%

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Multibit, Lead‐Free Cs₂SnI₆ Resistive Random Access Memory with Self‐Compliance for Improved Accuracy in Binary Neural Network Application

Kumar,

Krishnaiah,

Park

et al. 2023

Adv Funct Materials

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In the realm of neuromorphic computing, integrating Binary Neural Networks (BNN) with non‐volatile memory based on emerging materials can be a promising avenue for introducing novel functionalities. This study underscores the viability of lead‐free, air‐stable Cs2SnI6 (CSI) based resistive random access memory (RRAM) devices as synaptic weights in neuromorphic architectures, specifically for BNNs applications. Herein, hydrothermally synthesized CSI perovskites are explored as a resistive layer in RRAM devices either on the rigid or flexible substrate, highlighting reproducible multibit switching with self‐compliance, low‐ resistance‐state (LRS) variations, a decent On/Off ratio(or retention) of ≈103(or 104 s), and endurance exceeding 300 cycles. Moreover, a comprehensive evaluation with the 32 × 32 × 3 RGB CIFAR‐10 dataset reveals that binary convolutional neural networks (BCNN) trained solely on binary weight values can achieve competitive rates of accuracy comparable to those of their analog weight counterparts. These findings highlight the dominance of the LRS for CSI RRAM with self‐compliance in a weighted configuration and minimal influence of the high resistance state despite substantial fluctuations for flexible CSI RRAM under varying bending radii. With its unique electrical switching capabilities, the CSI RRAM is highly anticipated to emerge as a promising candidate for embedded AI systems, especially in IoT devices and wearables.

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Section: Rram Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multibit, Lead‐Free Cs₂SnI₆ Resistive Random Access Memory with Self‐Compliance for Improved Accuracy in Binary Neural Network Application

Kumar,

Krishnaiah,

Park

et al. 2023

Adv Funct Materials

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“…[ 15–18 ] Despite the wide range of advantages of memristive devices in neuromorphic computing, there are still several challenges at the device level such as poor stability, high variability, poor retention, and endurance. [ 19 ]…”

Section: Introductionmentioning

confidence: 99%

“…[ 18,27 ] Because of the stochastic and abrupt nature of these processes, large device‐to‐device and cycle‐to‐cycle variations have frequently been observed. [ 2,19 ] This further results in wide variations in the set/reset voltage, on/off ratio, endurance, retention and switching speed, and therefore, hindering their practical realization. [ 28–31 ] More recently, significant attention has been given to interface‐type (IT) RS devices, which exhibit uniform and nondestructive resistance change induced by an interface Schottky barrier modulation.…”

Section: Introductionmentioning

confidence: 99%

An Interface‐Type Memristive Device for Artificial Synapse and Neuromorphic Computing

Kunwar

Jernigan

Hughes

et al. 2023

Advanced Intelligent Systems

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Interface‐type (IT) metal/oxide Schottky memristive devices have attracted considerable attention over filament‐type (FT) devices for neuromorphic computing because of their uniform, filament‐free, and analog resistive switching (RS) characteristics. The most recent IT devices are based on oxygen ions and vacancies movement to alter interfacial Schottky barrier parameters and thereby control RS properties. However, the reliability and stability of these devices have been significantly affected by the undesired diffusion of ionic species. Herein, a reliable interface‐dominated memristive device is demonstrated using a simple Au/Nb‐doped SrTiO3 (Nb:STO) Schottky structure. The Au/Nb:STO Schottky barrier modulation by charge trapping and detrapping is responsible for the analog resistive switching characteristics. Because of its interface‐controlled RS, the proposed device shows low device‐to‐device, cell‐to‐cell, and cycle‐to‐cycle variability while maintaining high repeatability and stability during endurance and retention tests. Furthermore, the Au/Nb:STO IT memristive device exhibits versatile synaptic functions with an excellent uniformity, programmability, and reliability. A simulated artificial neural network with Au/Nb:STO synapses achieves a high recognition accuracy of 94.72% for large digit recognition from MNIST database. These results suggest that IT resistive switching can be potentially used for artificial synapses to build next‐generation neuromorphic computing.

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“…[2,3] In order to expand multi-bit storage capacity without sacrificing additional 2D area, it has been shown that resistive switching random access memory (RRAM) is capable of demonstrating multilevel memory states by modulating its internal state parameters. [4,5] The RRAM is one of the classes of memory concepts that is based on the electrical stimulation-induced change in resistance of a metal-insulator-metal (MIM) memory structure. [6,7] The crude classification of RRAM devices is based on the working principle, in which one device operates entirely on the migration and redistribution of cations, such as Cu or Ag, Mg, and Al, etc.…”

Section: Introductionmentioning

confidence: 99%

Multilevel Reset Dependent Set of a Biodegradable Memristor with Physically Transient

Shaikh,

Nguyen,

Jeon

et al. 2023

Advanced Science

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The electronic device, with its biocompatibility, biodegradability, and ease of fabrication process, shows great potential to embed into health monitoring and hardware data security systems. Herein, polyvinylpyrrolidone (PVP) biopolymer is presented as an active layer, electrochemically active magnesium (Mg) as a metal electrode, and chitosan‐based substrate (CHS) to fabricate biocompatible and biodegradable physically transient neuromorphic device (W/Mg/PVP/Mg/CHS). The I–V curve of device is non‐volatile bipolar in nature and shows a unique compliance‐induced multilevel RESET‐dependent‐SET behavior while sweeping the compliance current from a few microamperes to milliamperes. Non‐volatile and stable switching properties are demonstrated with a long endurance test (100 sweeps) and retention time of over 104 s. The physically transient memristor (PTM) has remarkably high dynamic RON/ROFF (ON/OFF state resistance) ratio (106 Ω), and when placed in deionized (DI) water, the device is observed to completely dissolve within 10 min. The pulse transient measurements demonstrate the neuromorphic computation capabilities of the device in the form of excitatory post synaptic current (EPSC), potentiation, depression, and learning behavior, which resemble the biological function of neurons. The results demonstrate the potential of W/Mg/PVP/Mg/CHS device for use in future healthcare and physically transient electronics.

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Cited by 3 publications

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Multibit, Lead‐Free Cs₂SnI₆ Resistive Random Access Memory with Self‐Compliance for Improved Accuracy in Binary Neural Network Application

Multibit, Lead‐Free Cs₂SnI₆ Resistive Random Access Memory with Self‐Compliance for Improved Accuracy in Binary Neural Network Application

An Interface‐Type Memristive Device for Artificial Synapse and Neuromorphic Computing

Multilevel Reset Dependent Set of a Biodegradable Memristor with Physically Transient

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Abstract: abstract

Cited by 3 publications

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Multibit, Lead‐Free Cs2SnI6 Resistive Random Access Memory with Self‐Compliance for Improved Accuracy in Binary Neural Network Application

Multibit, Lead‐Free Cs2SnI6 Resistive Random Access Memory with Self‐Compliance for Improved Accuracy in Binary Neural Network Application

An Interface‐Type Memristive Device for Artificial Synapse and Neuromorphic Computing

Multilevel Reset Dependent Set of a Biodegradable Memristor with Physically Transient

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Multibit, Lead‐Free Cs₂SnI₆ Resistive Random Access Memory with Self‐Compliance for Improved Accuracy in Binary Neural Network Application

Multibit, Lead‐Free Cs₂SnI₆ Resistive Random Access Memory with Self‐Compliance for Improved Accuracy in Binary Neural Network Application