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        <title>Frontiers in Nanotechnology | Nanoelectronics section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/nanotechnology/sections/nanoelectronics</link>
        <description>RSS Feed for Nanoelectronics section in the Frontiers in Nanotechnology journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-05-13T10:37:00.100+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnano.2025.1750193</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnano.2025.1750193</link>
        <title><![CDATA[Advances in laser-induced graphene: materials, fabrication, and emerging applications in flexible electronics]]></title>
        <pubdate>2026-01-16T00:00:00Z</pubdate>
        <category>Review</category>
        <author>In Jun Oh</author><author>Doyoun Kim</author><author>Seong-Yeop Kim</author><author>Sueun Choi</author><author>Woon-Hong Yeo</author><author>Hyo-Ryoung Lim</author>
        <description><![CDATA[Laser-induced graphene (LIG) has evolved from a rapid polymer-to-carbon conversion method into a versatile platform for fabricating high-performance flexible electronics. This review provides a comprehensive understanding of the photothermal and photochemical mechanisms governing LIG formation, emphasizing how laser parameters wavelength, fluence, and scanning speed determine graphitization pathways and resulting electrical characteristics. Beyond process fundamentals, we highlight recent advances in conductivity engineering achieved through pre- and post-treatment strategies, including metal nanoparticle incorporation, catalytic doping, and rapid Joule annealing. These modifications enable sheet resistances below 10 Ω/sq and significantly enhance electrochemical and mechanical performance. Finally, we discuss the integration of LIG in flexible sensors, energy harvesters, and bioelectronic systems, underscoring its scalability, design freedom, and environmental sustainability. By unifying insights across mechanism, processing, and application, this review outlines a coherent roadmap for harnessing LIG as a key material in next-generation soft electronics and wearable technologies.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnano.2025.1723433</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnano.2025.1723433</link>
        <title><![CDATA[Low-voltage-driven memristor framework for efficient neuromorphic computation with STDP functionality]]></title>
        <pubdate>2025-12-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Aarti Dahiya</author><author>Shalu Rani</author><author>Sanjay Kumar</author><author>Themis Prodromakis</author>
        <description><![CDATA[In this work, a low-voltage-driven theoretical memristor framework is presented with its in-depth parametric evaluation and its neuromorphic computing functionalities, including spike-time dependent plasticity (STDP) via Hebbian learning rules. The presented memristor model efficiently emulates the fundamental pinched hysteresis loop under the application of an input voltage amplitude of 10 mV, which enables its adaptability in low-voltage operation. Moreover, the memristor model efficiently emulates its response under the variations in the applied voltage, initial state variable, boundedness of state variable, control parameter for the rate of change of state variable, experimental fitting parameters, magnitude of exponentials, and conductivity slope parameters. These aforementioned parameters significantly affect the response of the memristor model, which further requires their optimization to understand their impact on the memristor characteristics. Therefore, these parameters are scrutinized based on their strong to weak impact on the memristor model response and its suitability in the neuromorphic computation. Additionally, the presented memristor model efficiently emulates various neuromorphic computing characteristics, including potentiation, depression, conductance tuneability, short-term memory (STM), long-term memory (LTM), transition from STM-to-LTM and vice versa, paired pulse facilitation (PPF), synaptic re-stimulation process, and STDP via Hebbian learning rules. Therefore, the presented theoretical memristor framework can be further useful in the in-memory computation circuit hardware, low-voltage logic operation, pattern recognition, and neuromorphic computing.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnano.2025.1702438</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnano.2025.1702438</link>
        <title><![CDATA[Tuning urine glucose sensing via metal films in graphene-oxide-based SPR architectures]]></title>
        <pubdate>2025-11-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Talia Tene</author><author>Stefano Bellucci</author><author>Marco Guevara</author><author>Paul Romero</author><author>Henry Sebastián Mayorga Pérez</author><author>Lala Gahramanli</author><author>Rana Khankishiyeva</author><author>Elfahem Sakher</author><author>Cristian Vacacela Gomez</author>
        <description><![CDATA[In this work, we analyze graphene-oxide (GO)-based surface plasmon resonance (SPR) stacks of fixed architecture (SF6/metal/Si3N4/GO) at 633 nm to isolate the role of the plasmonic film (Au, Ag, Cu, and Al) in urine glucose (UGLU) sensing. Transfer-matrix simulations, validated against reference SPR data, identify the thickness windows for each layer and benchmark the angular response across a clinically relevant concentration ladder. Metals separate by function: Au yields the largest resonance-angle shifts and the highest sensitivity; Cu and Al provide the narrowest linewidths, elevating detection accuracy and quality factor; Ag offers a balanced compromise with deep minima. These trends persist over the examined UGLU range and clarify that maximizing sensitivity does not always maximize resolvability under fixed angular noise. We outline an experimentally feasible route—low-temperature Si3N4, nm-scale GO coatings and ultrathin dielectric caps for base metals—together with strategies to address urine-matrix effects and paths toward selective operation (e.g., enzyme or receptor layers). The results supply fabrication-ready prescriptions and a metal-dependent design map for urine-based SPR sensing, which is suitable for extension to multi-wavelength interrogation when dispersion data are available.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnano.2025.1632279</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnano.2025.1632279</link>
        <title><![CDATA[A comparative study of flexible electrode design on the performance of flexible wearable electronics]]></title>
        <pubdate>2025-08-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Akib Abdullah Khan</author><author>Seunghyeb Ban</author><author>Woon-Hong Yeo</author><author>Jong-Hoon Kim</author>
        <description><![CDATA[Flexible wearable electronics are promising for continuous health monitoring, particularly in electromyography (EMG) applications. A critical factor in their performance is electrode design, which affects mechanical resilience and electrical stability. Here, this study develops multiple electrode geometries: open-mesh, closed-mesh, and island-bridge, fabricated from gold-coated polyimide substrates to offer the best performance in EMG detection. Under standardized bending and stretching tests, the island-bridge design shows the lowest resistance variation (±1.61%), while the closed-mesh design provides balanced performance across various strains. EMG tests indicate that the closed-mesh electrodes deliver the highest signal-to-noise ratios (up to 14.83 dB) with minimal motion artifacts. Although the open-mesh design is flexible, it has lower electrical stability. In summary, the closed-mesh performs best overall, the open-mesh is better for handling motion artifacts, and the island-bridge is ideal for areas with minimal movement.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnano.2025.1652480</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnano.2025.1652480</link>
        <title><![CDATA[Nanobiosensors for monitoring of stem-cell differentiation and organoids]]></title>
        <pubdate>2025-07-24T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Young Hoon Son</author><author>Gun-Jae Jeong</author>
        <description><![CDATA[Nanobiosensors now allow continuous, nondestructive tracking of stem cell differentiation and organoid maturation. Classical assays such as immunostaining and polymerase chain reaction are invasive snapshots that overlook fast molecular events guiding lineage choice. Nanoscale probes operate inside living constructs, translating genetic, metabolic, and mechanical signals into optical, electrical, or magnetic readouts while leaving viability intact. This review arranges recent progress by cell type. In pluripotent systems CRISPR Cas13a fluorescence resonance energy transfer beacons, single layer molybdenum disulfide nanopores, and dCas9 SunTag reporters reveal minute scale waves of microRNA and transcription factor activity, addressing teratoma risk. Mesenchymal stromal cells use locked nucleic acid beacons, piezoelectric scaffolds, and magnetic tracers to quantify Notch signaling, mechano sensing, and engraftment. Brain, cardiac, and vascular organoids adopt microneedle electrode arrays, stretchable optical membranes, and impedance chips to monitor deep electrophysiology, contractility, and barrier integrity, while quantum dots and metal organic frameworks combine delivery and sensing across other organoid models. Key hurdles remain, including lack of fabrication standards, uncertain probe occupancy limits, and unclear regulatory pathways. Multimodal chips, artificial intelligence driven analytics, and biodegradable sensor substrates offer potential solutions, moving nanobiosensors closer to routine clinical use.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnano.2025.1587700</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnano.2025.1587700</link>
        <title><![CDATA[Low-voltage programming of RRAM-based crossbar arrays using MOS parasitic diodes]]></title>
        <pubdate>2025-07-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sachin Maheshwari</author><author>Alex Serb</author><author>Themis Prodromakis</author>
        <description><![CDATA[Due to their high density, scalability, and low-power properties, 1-transistor-1-resistor (1T1R) RRAM-based crossbars have been exploited in the past. However, the series resistance of the transistor is a major problem in 1T1R crossbar arrays. This limits the maximum current available for inducing resistive switching and degrades the array’s performance. To mitigate this issue, we propose a new configuration—1-transistor-1-diode-1-resistor (1T1D1R)—in which diodes are used (including bulk source/drain parasitic diodes of the access transistor) to bypass the gating transistor during the programming operation (“write”). The proposed solution trades increased overhead in the layout area for a dramatic increase in the maximum achievable current drive on RRAM devices, resulting in the ability to deliver 1.5 mA+ with a voltage supply as low as 1.2 V using minimum-size devices (in our implementation). We designed a 32 × 32 crossbar array with on-chip peripheral circuitry in commercially available 0.18 μm triple-well CMOS technology for the proof of concept. We demonstrate bidirectional programming, showing a memristance change of ≈500 Ω for 120 and 80 pulses in positive and negative directions, respectively.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnano.2025.1627210</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnano.2025.1627210</link>
        <title><![CDATA[Reaching new frontiers in nanoelectronics through artificial intelligence]]></title>
        <pubdate>2025-06-25T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Santhosh Sivasubramani</author><author>Themis Prodromakis</author>
        <description><![CDATA[Artificial Intelligence (AI) is revolutionizing industries worldwide, delivering unprecedented productivity gains across diverse sectors, from healthcare to manufacturing. Recent advances in generative AI models have particularly accelerated innovation, enabling more efficient execution of complex tasks such as drug discovery, autonomous driving, and predictive maintenance. In the areas of electronics manufacturing: a sector crucial to the advancement of modern technologies, the impact of AI is profound, with the potential to transform every stage of the supply chain. This perspective investigates the role of AI in reshaping the electronics and semiconductor industries, exploring how it integrates into various stages of production and development. The approach to AI integration is structured and methodical, addressing both challenges and opportunities across five key nanotechnology areas: materials discovery, device design, circuit and system design, testing/verification, and modeling. In materials discovery, AI aids in identifying new, more efficient and sustainable materials. In device design, it enhances the functionality and integration of components. AI’s capabilities in circuit and system design enables more complex and precise electronic systems. During the testing and verification stage, AI contributes to more rigorous and faster testing processes, ensuring reliability before market release. Finally, in modeling, AI’s predictive capabilities allow for accurate simulations, crucial for anticipating performance under various scenarios. Each pillar of this electronics supply chain underscores AI’s ability to accelerate processes, optimize performance, and reduce costs. Supported by case studies of AI-driven breakthroughs, this perspective provides a comprehensive review of current AI applications across the entire electronic supply chain, illustrating improvements in yield and sustainable manufacturing practices.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnano.2025.1634033</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnano.2025.1634033</link>
        <title><![CDATA[Omnidirectionally stretchable, biodegradable mesh electrode with re-entrant structure for spatial-stable functional position on dynamic organs]]></title>
        <pubdate>2025-06-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jaewon Kim</author><author>Kyung Su Kim</author><author>Seungbin Kim</author><author>Yong-seok Lee</author><author>Jahyun Koo</author>
        <description><![CDATA[The electrode, interfacing with soft tissue, is vulnerable to mechanical failure caused by dynamic organ motions such as cardiac activity, respiration, and digestion. Mechanical mismatch can also lead to tissue damage and sensor displacement. However, existing strategies for conformal integration often fall short of preserving mechanical compliance across large-area, multi-electrode arrays. Most internal organs undergo complex, anisotropic volumetric expansion from physiological activity, requiring implanted systems that can withstand multidirectional strains without inducing stress concentration. Conventional elastomers and mesh-structured electrodes typically exhibit a positive Poisson’s ratio, which hinders multidirectional uniform stretching and results in mechanical mismatch at the tissue–electrode interface. This mismatch not only increases local mechanical load but also leads to electrode displacement. In this study, we propose a conformal electrode design that incorporates a re-entrant geometry into a stretchable and biodegradable polyurethane substrate. Mechanical testing confirmed that this geometry enhances stretchability and reduces the effective modulus of the electrode by approximately 64%. Furthermore, the device maintained electrical stability under cyclic deformation and preserved its structural integrity under dynamic, organ-mimicking volumetric expansion. This mechanical and electrical robustness highlights the potential of the proposed design for long-term integration into implantable electrode arrays for physiological monitoring and disease diagnosis on dynamic three-dimensional organ motion.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnano.2024.1505751</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnano.2024.1505751</link>
        <title><![CDATA[MoS2-based biosensor for SARS-CoV-2 detection: a numerical approach]]></title>
        <pubdate>2025-01-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Talia Tene</author><author>Gabriela Tubon-Usca</author><author>Katherine Tixi Gallegos</author><author>María José Mendoza Salazar</author><author>Cristian Vacacela Gomez</author>
        <description><![CDATA[Surface plasmon resonance (SPR) biosensors are powerful tools for highly sensitive and specific detection of biomolecules. This study introduces a MoS₂-based SPR biosensor optimized for SARS-CoV-2 detection. The sensor integrates a multilayer configuration, including a BK7 prism, Ag film (45 nm), S₃N₄ layer (13 nm), MoS₂ monolayer (0.65 nm), and functionalized ssDNA layer (5 nm). Systematic optimization of each layer improved plasmonic coupling, propagation, and specificity, achieving a balance between sensitivity, resolution, and efficiency. The optimized biosensor was evaluated across virus concentrations ranging from 0.01 to 150 mM. The proposed biosensor demonstrated excellent performance at moderate to high concentrations, with sensitivity up to 261.33°/RIU, a quality factor of 36.16 RIU−1, and a limit of detection of 1.91 × 10−5. An optimal figure of merit of 405.50 RIU−1 was achieved at 10 mM, highlighting the sensor’s diagnostic potential. However, challenges remain at very low concentrations (0.01–0.1 mM), where angular shifts, sensitivity, and signal-to-noise ratio were negligible.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnano.2024.1371386</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnano.2024.1371386</link>
        <title><![CDATA[Harnessing ferroic ordering in thin film devices for analog memory and neuromorphic computing applications down to deep cryogenic temperatures]]></title>
        <pubdate>2024-05-15T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Sayani Majumdar</author>
        <description><![CDATA[The future computing beyond von Neumann era relies heavily on emerging devices that can extensively harness material and device physics to bring novel functionalities and can perform power-efficient and real time computing for artificial intelligence (AI) tasks. Additionally, brain-like computing demands large scale integration of synapses and neurons in practical circuits that requires the nanotechnology to support this hardware development, and all these should come at an affordable process complexity and cost to bring the solutions close to market rather soon. For bringing AI closer to quantum computing and space technologies, additional requirements are operation at cryogenic temperatures and radiation hardening. Considering all these requirements, nanoelectronic devices utilizing ferroic ordering has emerged as one promising alternative. The current review discusses the basic architectures of spintronic and ferroelectric devices for their integration in neuromorphic and analog memory applications, ferromagnetic and ferroelectric domain structures and control of their dynamics for reliable multibit memory operation, synaptic and neuronal leaky-integrate-and-fire (LIF) functions, concluding with their large-scale integration possibilities, challenges and future research directions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnano.2024.1400666</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnano.2024.1400666</link>
        <title><![CDATA[2D MoS2 monolayers integration with metal oxide-based artificial synapses]]></title>
        <pubdate>2024-04-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mohit Kumar Gautam</author><author>Sanjay Kumar</author><author>Shalu Rani</author><author>Ioannis Zeimpekis</author><author>Dimitra G. Georgiadou</author>
        <description><![CDATA[In this study, we report on a memristive device structure wherein monolayers of two-dimensional (2D) molybdenum disulfide (MoS2) are integrated with an ultrathin yttrium oxide (Y2O3) layer to simulate artificial synapses functionality. The proposed physical simulation methodology is implemented in COMSOL Multiphysics tool and is based on the minimization of free energy of the used materials at the applied input voltage. The simulated device exhibits a stable bipolar resistive switching and the switching voltages is significantly reduced by increasing the number of MoS2 layers, which is key to conventional low-power computing and neuromorphic applications. The device is shown to perform synaptic functionalities under various applied bias conditions. The resulting synaptic weight decreases almost linearly with the increasing number of MoS2 layers due to the increase in the device thickness. The simulation outcomes pave the way for the development of optimised metal oxide-based memristive devices through their integration with semiconducting 2D materials. Also, the 2D MoS2 integration can enable the optoelectronic operation of this memory device.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnano.2023.1219975</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnano.2023.1219975</link>
        <title><![CDATA[Emerging quantum hybrid systems for non-Abelian-state manipulation]]></title>
        <pubdate>2023-10-13T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Bhaskaran Muralidharan</author><author>Manohar Kumar</author><author>Chuan Li</author>
        <description><![CDATA[The non-Abelian state has garnered considerable interest in the field of fundamental physics and future applications in quantum computing. In this review, we introduce the basic ideas of constructing the non-Abelian states in various systems from 1D to 3D and discuss the possible approaches to detect these states, including the Majorana bound states in a hybrid device and the v = 5/2 state in a fractional quantum Hall system.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnano.2023.1147396</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnano.2023.1147396</link>
        <title><![CDATA[Digital in-memory stochastic computing architecture for vector-matrix multiplication]]></title>
        <pubdate>2023-07-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shady Agwa</author><author>Themis Prodromakis</author>
        <description><![CDATA[The applications of the Artificial Intelligence are currently dominating the technology landscape. Meanwhile, the conventional Von Neumann architectures are struggling with the data-movement bottleneck to meet the ever-increasing performance demands of these data-centric applications. Moreover, The vector-matrix multiplication cost, in the binary domain, is a major computational bottleneck for these applications. This paper introduces a novel digital in-memory stochastic computing architecture that leverages the simplicity of the stochastic computing for in-memory vector-matrix multiplication. The proposed architecture incorporates several new approaches including a new stochastic number generator with ideal binary-to-stochastic mapping, a best seeding approach for accurate-enough low stochastic bit-precisions, a hybrid stochastic-binary accumulation approach for vector-matrix multiplication, and the conversion of conventional memory read operations into on-the-fly stochastic multiplication operations with negligible overhead. Thanks to the combination of these approaches, the accuracy analysis of the vector-matrix multiplication benchmark shows that scaling down the stochastic bit-precision from 16-bit to 4-bit achieves nearly the same average error (less than 3%). The derived analytical model of the proposed in-memory stochastic computing architecture demonstrates that the 4-bit stochastic architecture achieves the highest throughput per sub-array (122 Ops/Cycle), which is better than the 16-bit stochastic precision by 4.36x, while still maintaining a small average error of 2.25%.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnano.2023.1055527</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnano.2023.1055527</link>
        <title><![CDATA[Dopant network processing units as tuneable extreme learning machines]]></title>
        <pubdate>2023-03-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>B. van de Ven</author><author>U. Alegre-Ibarra</author><author>P. J. Lemieszczuk</author><author>P. A. Bobbert</author><author>H.-C. Ruiz Euler</author><author>W. G. van der Wiel</author>
        <description><![CDATA[Inspired by the highly efficient information processing of the brain, which is based on the chemistry and physics of biological tissue, any material system and its physical properties could in principle be exploited for computation. However, it is not always obvious how to use a material system’s computational potential to the fullest. Here, we operate a dopant network processing unit (DNPU) as a tuneable extreme learning machine (ELM) and combine the principles of artificial evolution and ELM to optimise its computational performance on a non-linear classification benchmark task. We find that, for this task, there is an optimal, hybrid operation mode (“tuneable ELM mode”) in between the traditional ELM computing regime with a fixed DNPU and linearly weighted outputs (“fixed-ELM mode”) and the regime where the outputs of the non-linear system are directly tuned to generate the desired output (“direct-output mode”). We show that the tuneable ELM mode reduces the number of parameters needed to perform a formant-based vowel recognition benchmark task. Our results emphasise the power of analog in-matter computing and underline the importance of designing specialised material systems to optimally utilise their physical properties for computation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnano.2023.1121492</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnano.2023.1121492</link>
        <title><![CDATA[Morphology control of volatile resistive switching in La0.67Sr0.33MnO3 thin films on LaAlO3 (001)]]></title>
        <pubdate>2023-02-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>A. Jaman</author><author>A. S. Goossens</author><author>J. J. L. van Rijn</author><author>L. van der Zee</author><author>T. Banerjee</author>
        <description><![CDATA[The development of in-memory computing hardware components based on different types of resistive materials is an active research area. These materials usually exhibit analog memory states originating from a wide range of physical mechanisms and offer rich prospects for their integration in artificial neural networks. The resistive states are classified as either non-volatile or volatile, and switching occurs when the material properties are triggered by an external stimulus such as temperature, current, voltage, or electric field. The non-volatile resistance state change is typically achieved by the switching layer’s local redox reaction that involves both electronic and ionic movement. In contrast, a volatile change in the resistance state arises due to the transition of the switching layer from an insulator to a metal. Here, we demonstrate volatile resistive switching in twinned LaAlO3 onto which strained thin films of La0.67Sr0.33MnO3 (LSMO) are deposited. An electric current induces phase transition that triggers resistive switching, close to the competing phase transition temperature in LSMO, enabled by the strong correlation between the electronic and magnetic ground states, intrinsic to such materials. This phase transition, characterized by an abrupt resistance change, is typical of a metallic to insulating behavior, due to Joule heating, and manifested as a sharp increase in the voltage with accompanying hysteresis. Our results show that such Joule heating-induced hysteretic resistive switching exhibits different profiles that depend on the substrate texture along the current path, providing an interesting direction toward new multifunctional in-memory computing devices.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnano.2023.1114267</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnano.2023.1114267</link>
        <title><![CDATA[Editorial: Advanced characterization methods for HfO2/ZrO2-based ferroelectrics]]></title>
        <pubdate>2023-01-24T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Patrick D. Lomenzo</author><author>Umberto Celano</author><author>Thomas Kämpfe</author><author>Sean R. C. McMitchell</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnano.2022.1092177</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnano.2022.1092177</link>
        <title><![CDATA[Epitaxial ferroelectric memristors integrated with silicon]]></title>
        <pubdate>2022-12-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Miguel Rengifo</author><author>Myriam H. Aguirre</author><author>Martín Sirena</author><author>Ulrike Lüders</author><author>Diego Rubi</author>
        <description><![CDATA[Neuromorphic computing requires the development of solid-state units able to electrically mimic the behavior of biological neurons and synapses. This can be achieved by developing memristive systems based on ferroelectric oxides. In this work we fabricate and characterize high quality epitaxial BaTiO3-based memristors integrated with silicon. After proving the ferroelectric character of BaTiO3 we tested the memristive response of LaNiO3/BaTiO3/Pt microstructures and found a complex behavior which includes the co-existence of volatile and non-volatile effects, arising from the modulation of the BaTiO3/Pt Schottky interface by the direction of the polarization coupled to oxygen vacancy electromigration to/from the interface. This produces remanent resistance loops with tunable ON/OFF ratio and asymmetric resistance relaxations. These properties might be harnessed for the development of neuromorphic hardware compatible with existing silicon-based technology.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnano.2022.1026286</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnano.2022.1026286</link>
        <title><![CDATA[Simulation of XRD, Raman and IR spectrum for phase identification in doped HfO2 and ZrO2]]></title>
        <pubdate>2022-11-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Alfred Kersch</author><author>Richard Ganser</author><author>Maximilian Trien</author>
        <description><![CDATA[Fluorite-structured hafnium and zirconia require different, complementary characterization methods to identify the numerous metastable phases. This is because of the many possible positions of the oxygen ions, which are difficult to observe directly. Ab initio simulations are useful to probe the corresponding XRD, Raman, and infrared spectra for fingerprints. However, the predictive power of theoretical methods is limited both by model errors and by boundary conditions such as defects, stresses, and morphology that are difficult to detect. We first consider the calculation of Raman and infrared spectra of the most interesting undoped phases of HfO2 and ZrO2, compare the results with known results, and discuss the uncertainties. Next, we consider the possibilities of classifying the phases using X-ray diffraction. To this end, we introduce the effects of doping, which increases the uncertainty due to structural disorder. For illustration, we examine a large data set of doped structures obtained with ab initio calculations. To make an unbiased assignment of phases, we use machine learning methods with clusters. The limits of X-ray diffraction spectroscopy are reached when phase mixtures are present. Resolution of single-phase polycrystalline samples may only be possible here if these three characterization methods are used.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnano.2022.1008266</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnano.2022.1008266</link>
        <title><![CDATA[Neural network learning using non-ideal resistive memory devices]]></title>
        <pubdate>2022-10-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Youngseok Kim</author><author>Tayfun Gokmen</author><author>Hiroyuki Miyazoe</author><author>Paul Solomon</author><author>Seyoung Kim</author><author>Asit Ray</author><author>Jonas Doevenspeck</author><author>Raihan S. Khan</author><author>Vijay Narayanan</author><author>Takashi Ando</author>
        <description><![CDATA[We demonstrate a modified stochastic gradient (Tiki-Taka v2 or TTv2) algorithm for deep learning network training in a cross-bar array architecture based on ReRAM cells. There have been limited discussions on cross-bar arrays for training applications due to the challenges in the switching behavior of nonvolatile memory materials. TTv2 algorithm is known to overcome the device non-idealities for deep learning training. We demonstrate the feasibility of the algorithm for a linear regression task using 1R and 1T1R ReRAM devices. Using the measured device properties, we project the performance of a long short-term memory (LSTM) network with 78 K parameters. We show that TTv2 algorithm relaxes the criteria for symmetric device update response. In addition, further optimization of the algorithm increases noise robustness and significantly reduces the required number of states, thereby drastically improving the model accuracy even with non-ideal devices and achieving the test error close to that of the conventional learning algorithm with an ideal device.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnano.2022.900592</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnano.2022.900592</link>
        <title><![CDATA[Physical modeling of HZO-based ferroelectric field-effect transistors with a WOx channel]]></title>
        <pubdate>2022-08-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xin Wen</author><author>Mattia Halter</author><author>Laura Bégon-Lours</author><author>Mathieu Luisier</author>
        <description><![CDATA[The quasistatic and transient transfer characteristics of Hf0.57Zr0.43O2 (HZO)-based ferroelectric field-effect transistors (FeFETs) with a WOx channel are investigated using a 2-D time-dependent Ginzburg-Landau model as implemented in a state-of-the-art technology computer aided design tool. Starting from an existing FeFET configuration, the influence of different design parameters and geometries is analyzed before providing guidelines for next-generation devices with an increased “high (RH) to low (RL)” resistance ratio, i.e., RH/RL. The suitability of FeFETs as solid-state synapses in memristive crossbar arrays depends on this parameter. Simulations predict that a 13 times larger RH/RL ratio can be achieved in a double-gate FeFET, as compared to a back-gated one with the same channel geometry and ferroelectric layer. The observed improvement can be attributed to the enhanced electrostatic control over the semiconducting channel thanks to the addition of a second gate. A similar effect is obtained by thinning either the HZO dielectric or the WOx channel. These findings could pave the way for FeFETs with enhanced synaptic-like properties that play a key role in future neuromorphic computing applications.]]></description>
      </item>
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