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1. Scalable 3D Ta:SiO _x Memristive Devices

   https://doi.org/10.1002/aelm.201800958  

   Jiang, Hao; Li, Can; Lin, Peng; Pi, Shuang; Yang, Jianhua_Joshua; Xia, Qiangfei (February 2019, Advanced Electronic Materials)

   Abstract A highly reliable memristive device based on tantalum‐doped silicon oxide is reported, which exhibits high uniformity, robust endurance (≈1 × 10⁹cycles), fast switching speed, long retention, and analog conductance modulation. Devices with junction areas ranging from microscale to as small as 60 × 15 nm²are fabricated and electrically characterized. ON‐/OFF‐ conductance and reset current show weak area dependence when the device is relatively large, and they become proportional to the device area when further scaled down. Two‐layer devices with repeatable switching behavior are achieved. The current study shows the potentials of Ta:SiO₂‐based 3D vertical devices for memory and computing applications. It also suggests that doping of the switching layer is an efficient approach to engineer the performance of memristive devices. 

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2. A Comparative Study of Digital Memristor‐Based Processing‐In‐Memory from a Device and Reliability Perspective

   https://doi.org/10.1002/aelm.202500348  

   Neuner, Thomas; Padberg, Henriette; Kornblum, Lior; Yalon, Eilam; Amiri, Pedram Khalili; Kvatinsky, Shahar (November 2025, Advanced Electronic Materials)

   As data‐intensive applications increasingly strain conventional computing systems, processing‐in‐memory (PIM) has emerged as a promising paradigm to alleviate the memory wall by minimizing data transfer between memory and processing units. This review presents the recent advances in both stateful and non‐stateful logic techniques for PIM, focusing on emerging nonvolatile memory technologies such as resistive random‐access memory (RRAM), phase‐change memory (PCM), and magnetoresistive random‐access memory (MRAM). Both experimentally demonstrated and simulated logic designs are critically examined, highlighting key challenges in reliability and the role of device‐level optimization in enabling scalable and commercial viable PIM systems. The review begins with an overview of relevant logic families, memristive device types, and associated reliability metrics. Each logic family is then explored in terms of how it capitalizes on distinct device properties to implement logic techniques. A comparative table of representative device stacks and performance parameters illustrates trade‐offs and quality indicators. Through this comprehensive analysis, the development of optimized, robust memristive devices for next‐generation PIM applications is supported. 

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3. Memristive devices with short-term and long-term memory behaviors for processing temporal information

   https://doi.org/10.1063/5.0175200  

   Ki, Seung Jun; Kim, Jisoo; Chen, Mingze; Liang, Xiaogan (November 2023, Applied Physics Letters)

   Memristors based on 2D semiconductors such as MoS2 and its derivative materials exhibit analog switching behaviors capable of emulating some synaptic functions, including short-term plasticity, long-term potentiation, and spike-time-dependent-plasticity. Additional investigation is needed to realize reliable control of such synaptic behaviors for practical device implementation. To meet this scientific need, we fabricated MoS2-based memristors and studied their paired-pulse facilitation (PPF) and long-term memory characteristics under different pulse programming settings. This research has provided a guideline for identifying the programming settings for different neuromorphic processes. For example, a specific setting resulting in PPF > 30% and long-term conductance change < 20% has been identified to be suited for processing real-time temporal information. Furthermore, this research also indicates that the MoS2 memristor keeps having an almost constant relative change in conductance but greatly enhanced drive current level under laser illumination. This behavior can enable an easy integration of such memristive devices with state-of-the-art controller circuits for practice neuromorphic control applications. 

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4. Enhanced non-volatile resistive switching performance through ion-assisted magnetron sputtering of TiN bottom electrodes

   https://doi.org/10.1038/s43246-025-00798-z  

   Bakhit, Babak; Hellenbrand, Markus; Tsai, Benson Kunhung; Choudhury, Abhijeet; Polcik, Peter; Kolozsvari, Szilard; Wang, Haiyan; Flewitt, Andrew J; MacManus-Driscoll, Judith L (December 2025, Communications Materials)

   Abstract Emerging non-volatile memristor-based devices with resistive switching (RS) materials are being widely researched as promising contenders for the next generation of data storage and neuromorphic technologies. Titanium nitride (TiN_x) is a common industry-friendly electrode system for RS; however, the precise TiN_xproperties required for optimum RS performance is still lacking. Herein, using ion-assisted DC magnetron sputtering, we demonstrate the key importance not only of engineering the TiN_xbottom electrodes to be dense, smooth, and conductive, but also understoichiometric in N. With these properties, RS in HfO₂-based memristive devices is shown to be optimised for TiN_0.96. These devices have switching voltages ≤ ±1 V with promising device-to-device uniformity, endurance, memory window of ~40, and multiple non-volatile intermediate conductance levels. This study highlights the importance of precise tuning of TiN_xbottom electrodes to achieve robust performance of oxide resistive switching materials. 

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5. Inkjet-Printed Memristor Device for Neuromorphic Computing: Fabrication, Modeling, and Reservoir Implementation

   https://doi.org/10.1142/S0129156426400215  

   Adry, Tasnim Zaman; Gardner, Steven; Eliza, Sazia; Haider, Mohammad (July 2025, International Journal of High Speed Electronics and Systems)

   Co-localized memory and processing components are necessary for the creation of scalable neuromorphic hardware, and these components can be produced using inexpensive techniques. While memristors have emerged as promising candidates for synaptic emulation, most devices rely on complex fabrication processes that hinder large-scale deployment. This work presents a fully inkjet-printed memristor utilizing hexagonal boron nitride (hBN) and graphene inks, demonstrating a manufacturable approach to neuromorphic systems. The devices exhibit nonvolatile resistive switching with symmetric pinched hysteresis loops [Formula: see text] and tunable conductance states. Furthermore, a piece-wise nonlinear memristor model was developed and implemented in Simscape to enable system-level simulations. A [Formula: see text] reservoir computing architecture, constructed using these memristive devices, successfully demonstrated temporal pattern processing and feature extraction capabilities. The results establish printed memristors as viable building blocks for next-generation edge computing applications, combining the advantages of solution-processable materials with neuromorphic functionality. 

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