A Solution-Processed Ti/Pt/NiOx/Ag RRAM Device with Multiple Forming-Free Non-Volatile Intermediate States
Abstract
Artificial neural networks (ANN) implemented on crossbar arrays of non-volatile synaptic devices are known to consume much less energy compared to that implemented on conventional computing units. Solution-processed resistive random-access memory (RRAM) devices based on transition-metal oxides are suitable as such synaptic devices because of their cheap and easy fabrication method, stability, and suitable memristive properties. Here, in NiO-based RRAM devices with Ti/Pt as the bottom electrode and Ag as the top electrode (NiO layer deposited through solution processing), we experimentally report forming-free binary switching between a high resistance state (HRS) and a low resistance state (LRS) as well as forming-free pulse-driven modulation (long-term potentiation (LTP), long-term depression (LTD)) through 30 intermediate synaptic states between HRS and LRS (around 5-bit synaptic weight resolution). Many of these states are measured to be stable at least up to 1000 seconds confirming their non-volatility and their utility in storage of synaptic weights for ANN crossbar arrays. On the contrary, most prior experimental reports (including reports both on solution-processing-based and sputtering-based RRAM devices) require forming to exhibit non-volatility. Avoiding forming reduces variability, eliminates the need for high voltage pulses, and makes the crossbar array design simpler. Further, we confirm the presence of oxygen vacancies in our materials stack using X-ray photo-electron spectroscopy (XPS). Finally, we show successful data classification on MNIST and CIFAR-10 data sets using a multi-layer perceptron (MLP) model and a convolution neural network (CNN) model respectively, where the synaptic weight updates of these ANN models are calibrated against the experimentally measured LTP-LTD characteristics of the NiO RRAM devices.
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BibTeX
@misc{roy2026solutionprocessed,
title = {{A Solution-Processed Ti/Pt/NiOx/Ag RRAM Device with Multiple Forming-Free Non-Volatile Intermediate States}},
author = {Abhrajit Roy and Chopali Chanchal Sahu and Rudraksh Sangore and Raman Hissariya and Poulomi Chakrabarty and Harsh Roniyar and Merlyne De Souza and Debanjan Bhowmik},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.14322},
eprint = {2610.14322},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.14322},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - A Solution-Processed Ti/Pt/NiOx/Ag RRAM Device with Multiple Forming-Free Non-Volatile Intermediate States
AU - Abhrajit Roy
AU - Chopali Chanchal Sahu
AU - Rudraksh Sangore
AU - Raman Hissariya
AU - Poulomi Chakrabarty
AU - Harsh Roniyar
AU - Merlyne De Souza
AU - Debanjan Bhowmik
PY - 2026
DA - 2026/10/09
PB - arXiv
DO - 10.48550/arxiv.2610.14322
UR - https://arxiv.org/abs/2610.14322
AB - Artificial neural networks (ANN) implemented on crossbar arrays of non-volatile synaptic devices are known to consume much less energy compared to that implemented on conventional computing units. Solution-processed resistive random-access memory (RRAM) devices based on transition-metal oxides are suitable as such synaptic devices because of their cheap and easy fabrication method, stability, and suitable memristive properties. Here, in NiO-based RRAM devices with Ti/Pt as the bottom electrode and Ag as the top electrode (NiO layer deposited through solution processing), we experimentally report forming-free binary switching between a high resistance state (HRS) and a low resistance state (LRS) as well as forming-free pulse-driven modulation (long-term potentiation (LTP), long-term depression (LTD)) through 30 intermediate synaptic states between HRS and LRS (around 5-bit synaptic weight resolution). Many of these states are measured to be stable at least up to 1000 seconds confirming their non-volatility and their utility in storage of synaptic weights for ANN crossbar arrays. On the contrary, most prior experimental reports (including reports both on solution-processing-based and sputtering-based RRAM devices) require forming to exhibit non-volatility. Avoiding forming reduces variability, eliminates the need for high voltage pulses, and makes the crossbar array design simpler. Further, we confirm the presence of oxygen vacancies in our materials stack using X-ray photo-electron spectroscopy (XPS). Finally, we show successful data classification on MNIST and CIFAR-10 data sets using a multi-layer perceptron (MLP) model and a convolution neural network (CNN) model respectively, where the synaptic weight updates of these ANN models are calibrated against the experimentally measured LTP-LTD characteristics of the NiO RRAM devices.
ER -