A Comprehensive Study of Effect of Composition on Resistive Switching of HfxAl1-xOy based RRAM devices by Combinatorial Sputtering

Kumbhare, Pankaj; Meihar, Paritosh; Rajarathinam, Senthilkumar; Chouhan, S.; Pai, Suhit; Panwar, Neeraj; Ganguly, Udayan

doi:10.1557/opl.2015.82

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…7. In comparison, experimental switching characteristics of HfO 2 RRAM [19] show more asymmetric and voltage-dependent set/reset. To study the effect of a realistic RRAM, a circuit model with switching threshold varying with its conductance was developed to match experimental IV characteristics.…”

Section: Effects Of Realistic Rrammentioning

confidence: 87%

A software-equivalent SNN hardware using RRAM-array for asynchronous real-time learning

Shukla¹,

Kumar²,

Ganguly³

2017

2017 International Joint Conference on Neural Networks (IJCNN)

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Abstract-Spiking Neural Network (SNN) naturally inspires hardware implementation as it is based on biology. For learning, spike time dependent plasticity (STDP) may be implemented using an energy efficient waveform superposition on memristor based synapse. However, system level implementation has three challenges. First, a classic dilemma is that recognition requires current reading for short voltagespikes which is disturbed by large voltage-waveforms that are simultaneously applied on the same memristor for real-time learning i.e. the simultaneous read-write dilemma. Second, the hardware needs to exactly replicate software implementation for easy adaptation of algorithm to hardware. Third, the devices used in hardware simulations must be realistic. In this paper, we present an approach to address the above concerns. First, the learning and recognition occurs in separate arrays simultaneously in real-time, asynchronously -avoiding nonbiomimetic clocking based complex signal management. Second, we show that the hardware emulates software at every stage by comparison of SPICE (circuit-simulator) with MATLAB ® (mathematical SNN algorithm implementation in software) implementations. As an example, the hardware shows 97.5% accuracy in classification which is equivalent to software for a Fisher's Iris dataset. Third, the STDP is implemented using a model of synaptic device implemented using HfO 2 memristor. We show that an increasingly realistic memristor model slightly reduces the hardware performance (85%), which highlights the need to engineer RRAM characteristics specifically for SNN.

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Section: Effects Of Realistic Rrammentioning

confidence: 87%

A software-equivalent SNN hardware using RRAM-array for asynchronous real-time learning

Shukla¹,

Kumar²,

Ganguly³

2017

2017 International Joint Conference on Neural Networks (IJCNN)

Self Cite

View full text Add to dashboard Cite

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The First Principle Study of Comparison of Divalent and Trivalent Impurity in Rram Devices Using Gga+u

et al. 2021

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Resistive switching (RS) performances had prodigious attention due to their auspicious potential for data storage. Oxide-based devices with metal insulator metal (MIM) structure are more valuable for RS applications. In this study, we have studied the effect of divalent (nickel) as well as trivalent (aluminum) dopant without and with oxygen vacancy (V[Formula: see text] in hafnia (HfO[Formula: see text]-based resistive random-access memory (RRAM) devices. All calculations are carried out within the full potential linearized augmented plane-wave (FP-LAPW) method based on the WIEN2k code by using generalized gradient approximation (GGA) and generalized gradient approximation with U Hubbard parameters (GGA+U) approach. The studies of the band structure, density of states and charge density reveal that HfNiO2+Vo are more appropriate dopant to enhance the conductivity for RRAM devices.

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A Comprehensive Study of Effect of Composition on Resistive Switching of Hf_xAl_1-xO_y based RRAM devices by Combinatorial Sputtering

Cited by 2 publications

References 3 publications

A software-equivalent SNN hardware using RRAM-array for asynchronous real-time learning

A software-equivalent SNN hardware using RRAM-array for asynchronous real-time learning

The First Principle Study of Comparison of Divalent and Trivalent Impurity in Rram Devices Using Gga+u

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