Nanoscale RRAM-based synaptic electronics: toward a neuromorphic computing device
Open accessAbstract
Efforts to develop scalable learning algorithms for implementation of networks of spiking neurons in silicon have been hindered by the considerable footprints of learning circuits, which grow as the number of synapses increases. Recent developments in nanotechnologies provide an extremely compact device with low-power consumption.In particular, nanoscale resistive switching devices (resistive random-access memory (RRAM)) are regarded as a promising solution for implementation of biological synapses due to their nanoscale dimensions, capacity to store multiple bits and the low energy required to operate distinct states. In this paper, we report the fabrication, modeling and implementation of nanoscale RRAM with multi-level storage capability for an electronic synapse device. In addition, we first experimentally demonstrate the learning capabilities and predictable performance by a neuromorphic circuit composed of a nanoscale 1 kbit RRAM cross-point array of synapses and complementary metal-oxide-semiconductor neuron circuits. These developments open up possibilities for the development of ubiquitous ultra-dense, ultra-low-power cognitive computers.
AI snapshot
Written from the paper’s full text.
TL;DR
The paper reports fabrication, modeling, and circuit-level testing of nanoscale RRAM devices as electronic synapses for neuromorphic computing. It demonstrates a hybrid system combining a 1 kbit RRAM cross-point array with CMOS neuron circuits and shows learning/testing operation.
Key findings
- A 1 kbit cross-point RRAM array was fabricated with W/Al/Pr₀.₇Ca₀.₃MnO₃/Pt devices having diameters from 150 nm to 1 µm; the selector-less array uses nonlinear current-voltage behavior to reduce parasitic current paths.
- The devices exhibit analog multi-level conductance attributed to AlOₓ oxidation/reduction at the Al/PCMO interface; under double voltage sweeps, both current level and R_OFF/R_ON ratio increase gradually as the maximum electric field increases.
- A Verilog-A model based on Poole-Frenkel emission and space-charge-limited current was developed in Cadence and matched experimental data, enabling simulation of RRAM-based synaptic networks.
- A hybrid CMOS neuron/RRAM synapse circuit using a leaky integrate-and-fire model and 30 RRAM synapses produced five output states (Vout1–Vout5) in testing mode, with linear slopes indicating stable resistance and favorable retention/learning properties.
Methods
- Fabrication: bottom Pt (50 nm), polycrystalline PCMO (30 nm, RF magnetron sputtered at 600 °C), SiNₓ (80 nm, PECVD), via-hole formation, annealing at 500 °C for 30 min in oxygen, and top Al (7 nm)/W (90 nm); electrical measurements used an Agilent 4155A analyzer and 81104A pulse generator.
- Modeling: a Verilog-A RRAM synapse model in Cadence using Poole-Frenkel and space-charge-limited current equations, with oxide thickness and barrier height as key variables.
- Circuit testing: a CMOS neuron and RRAM array synapse configuration based on a leaky integrate-and-fire neuron, with learning and testing modes controlled by different clock cycles; sequential inputs from 0 to 9 were applied to update RRAM resistance.
Limitations
- The paper does not include an explicit limitations section.
- Evident from the text: the experimental demonstration is limited to a 1 kbit array and a 30-synapse branch, while the introduction notes biological synapse density is estimated at 10¹⁰ cm⁻² in the human cortex.
- The testing-mode circuit is described as encoding only ON/OFF synaptic states, although multi-level analog states were shown separately; the paper also notes a gap between semiconductor device fabrication and large-scale neuromorphic system design.
Model-written summary. Check it against the paper before relying on it.
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Cites
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Claims the paper makes
0 of 11 cited sources checked · 0 supported · 0 flagged
- Findingown data
The hybrid CMOS neuron/RRAM synapse circuit in testing mode produced five states (Vout1 to Vout5).
“There are five states (from Vout 1 to Vout 5 ).”
Quote in the paperNot a substantial claim- Rests on an assumption: Larger RRAM conductance corresponds to larger synaptic weights, representing a larger number of open channels in real synapses.
- Findingown data
The study experimentally demonstrated for the first time learning capabilities and predictable performance in a neuromorphic circuit made of a nanoscale 1 kbit RRAM cross-point array synapse network and CMOS neuron circuits.
“In this study, we experimentally demonstrated for the first time the learning capabilities and predictable performance in a neuromorphic circuit consisting of a nanoscale 1 kbit RRAM cross-point array synapse network and CMOS neuron circuits.”
- Also stated in: § Abstract · boosted
“In addition, we first experimentally demonstrate the learning capabilities and predictable performance by a neuromorphic circuit composed of a nanoscale 1 kbit RRAM cross-point array of synapses and complementary metal-oxide-semiconductor neuron circuits.”
Quote in the paperSubstantial claim- Rests on an assumption: Progress in implementing neuromorphic hardware systems has been hindered by the difficulty of fabricating electronic synapses because such structures require a very large area.
- Rests on an assumption: Implementation of fully functional large-scale neuromorphic systems that mimic brain functions is limited by a considerable gap between the ability to build semiconductor devices and the ability to design systems.
- Also stated in: § Abstract · boosted
- Findingreasoning
The resistance of RRAM-based synapses is closely related to the metal oxide layer thickness.
“The modeling results showed that the resistance of RRAM-based synapses is closely related to the metal oxide layer thickness.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: The barrier height varies linearly with the thickness of the oxide.
- Findingown data
The selector-less cross-point 1 kbit RRAM array displays analog memory characteristics.
“This graph shows the analog memory characteristics of the selector-less, cross-point 1 kbit RRAM array.”
Quote in the paperSubstantial claim - Findingown data
The proposed device exhibits multi-level I-V characteristic curves under the double sweep mode.
“Figure 5(a) shows the multi-level I-V characteristic curves of the proposed device under the double sweep mode.”
Quote in the paperSubstantial claim
- Premisecitation
An artificial brain needs a large number and high density of synapses.
“An artificial brain requires a considerable number and density of synapses (e.g., the density is estimated to be 10 10 cm -2 in the human cortex)”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [11] Seo J, 2011, Proc. of the Custom Integrated Circuits Conf. (CICC)
Not checked in the free run.
- Study design and conductown data
Testing showed that the Verilog-A model could accurately simulate the experimental data.
“As testing of the capability of the Verilog-A model showed that it could be used to accurately simulate the experimental data, we utilized it as the interface for simulating RRAM-based synaptic networks with CMOS-based neurons and for testing neuromorphic circuits.”
Quote in the paperSubstantial claim- The authors state this as an assumption: The barrier height varies linearly with the thickness of the oxide.
- Interpretationcitation
The multi-level conductance in the RRAM array results from oxidation and reduction of AlOx at the Al/PCMO interface.
“In light of the previously studied operational mechanisms for this type of device, the results show multi-level conductance caused by the oxidation and reduction of AlO x at the Al/PCMO interface [26].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [26] Jo M, 2010, Tech. Dig. VLSI Symp.
Not checked in the free run.
- Rests on an assumption: The switching behavior of PCMO devices is mainly controlled by the migration of oxygen ions under an electric field applied between the active top electrode and the PCMO film.
- Interpretationown data
RRAM-based synaptic electronics are well suited for future data storage and neuromorphic applications.
“The results of our work strongly suggest that RRAM-based synaptic electronics are well suited for use in future data storage and neuromorphic applications.”
Quote in the paperSubstantial claim- Rests on an assumption: Progress in implementing neuromorphic hardware systems has been hindered by the difficulty of fabricating electronic synapses because such structures require a very large area.
- Rests on an assumption: Implementation of fully functional large-scale neuromorphic systems that mimic brain functions is limited by a considerable gap between the ability to build semiconductor devices and the ability to design systems.
- Interpretationcitation
A negative bias at the top electrode moves oxygen ions from AlOx to the PCMO bulk layer, producing the low-resistance state.
“As a negative bias is applied at the top electrode, oxygen ions (O 2-) move from the AlO x to the PCMO bulk layer, which forms the LRS.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [26] Jo M, 2010, Tech. Dig. VLSI Symp.
Not checked in the free run.
- Rests on an assumption: The switching behavior of PCMO devices is mainly controlled by the migration of oxygen ions under an electric field applied between the active top electrode and the PCMO film.
- Premisecitation
Using the nonlinear current-voltage characteristics of some resistive switches is a better way to break sneak current paths than relying on external diodes as selectors.
“Instead of relying on external diodes as selectors, a better approach to breaking sneak current paths is to take advantage of the nonlinear current-voltage (I-V) characteristics inherent to some types of resistive switches [18][19][20][21].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [18][19][20][21]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- May combine two claims. 6
- Classified as probably not a substantial claim. 3
May combine two claims.
Findingown dataAs the electric field (Vmax) increases, both the current level and the R_OFF/R_ON ratio increase gradually.
“Both the current level and R OFF /R ON ratio are increased gradually when the electric field (V max ) increases.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationBiological synaptic structures are dense, use very little power, and retain memory for long periods.
“biological synaptic structures are not only dense but also consume miniscule amounts of power and maintain memory storage for long periods of time [11].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [11] Seo J, 2011, Proc. of the Custom Integrated Circuits Conf. (CICC)
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationBiologically inspired neuromorphic systems have drawn significant interest as a way to address both the scaling and energy-efficiency problems.
“The development of biologically inspired neuromorphic systems has attracted considerable interest over the last few years as a means of overcoming both issues [6][7][8][9][10].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [6][7][8][9][10]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe density of synapses in the human cortex is estimated to be 10^10 cm^-2.
“(e.g., the density is estimated to be 10 10 cm -2 in the human cortex)”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [11] Seo J, 2011, Proc. of the Custom Integrated Circuits Conf. (CICC)
Not checked in the free run.
May combine two claims.
PremisecitationCurrent semiconductor technology faces two main problems: the physical limits of device scaling and the poor energy efficiency of von Neumann systems relative to biological systems.
“Two critical issues associated with the current semiconductor technology are the physical scaling limits of device dimensions and the low energy efficiency of von Neumann systems as compared to those of biological systems [1][2][3][4][5].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [1][2][3][4][5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
Interpretationown dataThe linear slope of each curve indicates a constant resistance in the RRAM synapse, which in turn indicates favorable retention and learning properties.
“It is worth noting here that the slope of each curve shows linear behavior; this indicates that there is a constant value of resistance in an RRAM synapse, which in turn shows the favorable retention and learning properties of such synapses.”
- Also stated in: § 4. Conclusion · plain
“Each slope is linear.This means that the resistance of the RRAM synapse has good retention and learning properties.”
Quote in the paperSubstantial claim- Rests on an assumption: Larger RRAM conductance corresponds to larger synaptic weights, representing a larger number of open channels in real synapses.
- Also stated in: § 4. Conclusion · plain
May combine two claims.
InterpretationcitationA positive bias attracts oxygen ions and forms a thick insulating oxide layer, producing the high-resistance state by blocking conducting electrons.
“In contrast, a positive bias attracts O 2-and forms a thick insulating oxide layer, which results in an HRS by preventing conducting electrons.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [26] Jo M, 2010, Tech. Dig. VLSI Symp.
Not checked in the free run.
- Rests on an assumption: The switching behavior of PCMO devices is mainly controlled by the migration of oxygen ions under an electric field applied between the active top electrode and the PCMO film.
May combine two claims.
PremisecitationPassive RRAM arrays can form sneak paths, parasitic currents that interfere between neighboring cells, and this can make the array lose functionality.
“However, a fundamental problem faced by such passive arrays is that 'sneak paths' (parasitic current paths that cause interference between neighboring cells within the array) can be formed, causing the array to lose functionality [12,13].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [12] Lee M J, 2007, IEDM Tech. Dig.
Not checked in the free run.
- cites [13] source
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningIn the Poole-Frenkel emission and space charge-limited current models, the current level depends on the oxide thickness and the barrier height.
“As can be seen from equations ( 1)-( 2), the level of current in the Poole-Frenkel (PF) emission and space charge-limited current (SCLC) models is influenced by the oxide thickness (d i ) and the barrier height ( B ).”
Quote in the paperNot a substantial claim- The authors state this as an assumption: The barrier height varies linearly with the thickness of the oxide.
The paper relies on this without support.
Progress in implementing neuromorphic hardware systems has been hindered by the difficulty of fabricating electronic synapses because such structures require a very large area.
“However, progress in implementing neuromorphic hardware systems has been hindered by the difficulty associated with fabricating 'synapses' in electronic circuits, as such structures simply require a very large area.”
The paper relies on this without support.
Integrating a selection device into each cell increases the complexity and cost of the fabrication process.
“However, integration of a selection device increases the complexity and cost of the fabrication process.”
The paper relies on this without support.
Implementation of fully functional large-scale neuromorphic systems that mimic brain functions is limited by a considerable gap between the ability to build semiconductor devices and the ability to design systems.
“Ultimately, the implementation of fully functional large-scale neuromorphic systems that can mimic brain functions is limited by the considerable gap that currently exists between the ability to build semiconductor devices and the ability to design systems.”
The paper relies on this without support.
The switching behavior of PCMO devices is mainly controlled by the migration of oxygen ions under an electric field applied between the active top electrode and the PCMO film.
“The switching behavior of PCMO devices is mainly controlled by the migration of oxygen ions under an electric field applied between the active top electrode and the PCMO film.”
The authors state this as an assumption.
The barrier height varies linearly with the thickness of the oxide.
“Here, it is assumed that the barrier height varies linearly with the thickness of the oxide.”
The paper relies on this without support.
Larger RRAM conductance corresponds to larger synaptic weights, representing a larger number of open channels in real synapses.
“Larger RRAM conductance, which represents a larger number of open channels in real synapses, corresponds to larger synaptic weights.”
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“For the realization of a neuromorphic computing system, emerging electronic devices, including RRAM, have shown excellent features in emulating the essential functions of the biological brain [5].”
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- Authors
- Park, S., Noh, J., Choo, M.-L., Sheri, A. M., Chang, M., Kim, Y.-B., Kim, C. J., Jeon, M., Lee, B.-G., Lee, B. H., & Hwang, H.
- Affiliations
- Gwangju Institute of Science and Technology (KR)
- Pohang University of Science and Technology (KR)
- Corresponding author
- Hwang, Hyunsang
- Venue
- Nanotechnology
- Publisher
- IOP Publishing
- Published
- 2013-09-02
- Volume
- 24
- Issue
- 38
- Pages
- 384009
- ISSN
- 0957-4484 (ISSN-L)
- 1361-6528
- OpenAlex
- W2044709477
- Cited by
- 139
- References
- 19
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 10 · 2023: 9 · 2022: 12 · 2021: 11 · 2020: 13 · 2019: 19 · 2018: 14 · 2017: 13 · 2016: 10 · 2015: 7 · 2014: 10
- FWCI
- 5.39
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nanotechnology (publishedVersion)
- PubMed (publishedVersion)
- Open Access System for Information Sharing (Pohang University of Science and Technology) (submittedVersion)
- Funders
- National Research Foundation (2012-0009460)
- National Research Foundation of Korea (2012-0009460)
- Ministry of Education, Science and Technology
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
- Keywords
- resistive random access memory, electronic synapses, neuromorphic computing, spiking neurons, multilevel storage, cross-point array, learning algorithms, energy-efficient computing, artificial synapses
- MeSH terms
- Electronics, Models, Neurological, Silicon, Synapses, Neural Networks, Computer, Nanotechnology
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