Manel Bouzouita1†
Shashikant Pathak
Fakhreddine Zayer
Ioulia Tzouvadaki- 1Laboratoire d’Electroniques et Micro-électroniques (LR99ES30), Faculty of Sciences Of Monastir, University of Monastir, Monastir, Tunisia
- 2Center for Microsystems Technology and IMEC, University of Ghent, Ghent, Belgium
- 3National Institute of Technologies and Sciences of Elkef, University of Elkef, Elkef, Tunisia
In recent years, the interest of science in big data sensing, storage and processing has been growing fast. Nano-materials have been widely used in resistive switching devices thanks to their distinguished properties. Furthermore, they provide nano-scale dimensions and compatibility with fabrication procedures and complementary metal oxide semiconductor (CMOS) technology. Nano-materials can also enhance the performance of memristive structures. The operation of a memristor, which enables efficient resistive switching characterized by fast response, increased storage density, and low power requirements, depends largely on nano-materials and deposition techniques. Herein, a comprehensive brief review of nano-material RRAM arrays and their application in biomedical is discussed. First, we introduce planar and array resistive switching structures. Second, we report the different nanomaterial categories that can be used in resistive random-access memories (RRAMs). Then, we focus on the integration of 3D nano-material-based memristive crossbars for in-memory computing and biosensing arrays and discuss representative applications. The exploration of nano-materials enables the development of enhanced resistive switching architectures with increased signal integrity, great speed, and ultra-high sensitivity towards thermally and electrically stable memristive biomedical platforms.
1 Introduction
Since their breakthrough in 2008, HP memristors have been broadly used in various applications (Strukov et al., 2008), including analogue and digital circuits, biosensors, artificial neurons and synapses, as well as RRAMs for memory storage (Homsi et al., 2023; Li et al., 2018; Barraj et al., 2024). Resistive switching devices, often referred to as non-volatile memories, consist of sandwiched structures evolving metal-insulator-metal materials. They store data by alternating the insulator material’s resistive level between high and low states. RRAMs are characterised by rapid switching speed and ON/OFF ratio, efficient endurance, and long retention time (Bouzouita et al., 2024). These memristive operating standards are often affected by different properties of nanomaterials (Zahoor et al., 2020). In other words, memristive benchmarks interconnect the choice of nanomaterials with memristor performance (Wang Miao et al., 2018) through the diversity of intrinsic physical and chemical reactions of materials, as well as fabrication techniques and defect engineering that benefit the development and destruction of the conductive filament within the insulator layer. Various review papers on nanomaterials for memristive architectures exist in this context. In 2018, Ahn et al. explored
2 Introduction to RRAM
2.1 RRAM structure
Various RRAM structures have recently been investigated to enhance performance and address emerging computational and storage challenges. This paper discusses three primary RRAM architectures: (a) metal-insulator-metal (MIM) or planar, (b) Crossbar arrays, and (c) three-dimensional (3D) RRAM.
2.1.1 Planar structure
In 1967, Simmons et al. introduced the first proposed MIM structure (Figure 1a) in which resistive switching is attributed to the formation and rupture of conductive filaments in the insulating layer (Simmons and Verderber, 1967; Sawa, 2008). The MIM structure has shown high endurance
Figure 1. Comprehensive overview of RRAM structures, nano-materials, and characteristics. (a) Conductive filament dynamics and resistive switching processes. (a.I): Atomic-scale filament formation. (a.II): Operational phases. (b) Crossbar topology with word lines (WLs) and bit lines (BLs) and voltage distribution in the reading scheme. (c) RRAM crossbar array architectures and scaling approaches. (c.I): Planar crossbar array: Single-layer configuration with memory cells at word-line bit-line intersections. (c.II): Horizontal 3D stacking (HRRAM). (c.III): Vertical 3D architecture (VRRAM). (d) 0D nano-materials used in RRAMs. (e) 1D nano-materials used in RRAMs. (f) 2D nano-materials used in RRAMs. (g) Proposed M3D-LIME Chip Architecture with multiple layers. (h) RRAM-based Processing-in-Memory (PIM) Architecture. (i) RRAM array for multisensory perception. (j) Device-level calibration of the PSA sensing array baseline. (k) Sneak path effect that corrupts the stored data and lead to erroneous readouts. (l) examples of RRAM challenges. (m) Proposed solution: synthesis methods and applications of metal-organic frameworks (MOFs). (a) Reprinted from Sawa (2008). Copyright (2008), with permission from Elsevier. (b, c, g, h, j, k, l, m) reprinted with modification and permission. “Creative Commons Attribution” from (Chen et al., 2024; Wang Chenyu et al., 2023; Wang Hongzhe et al., 2023; Li Yijun et al., 2023; Tzouvadaki et al., 2020a; Shi et al., 2020; Adam et al., 2018; Li et al., 2024). (i) Reprinted from Zhu et al. (2022). Copyright (2022), with permission from John Wiley and Sons.
2.1.2 Crossbar array structure
In crossbar array structures (Figure 1b), RRAM cells are placed at the intersections of perpendicular electrode lines, allowing a higher integration density than the planar configuration. Crossbar arrays have the potential for cell sizes as small as 4F2, where F is the minimum feature size, resulting in storage densities exceeding 100 Gb/cm2 (Ali et al., 2016). However, crossbar arrays are plagued by sneak path currents that corrupt stored data and can lead to erroneous readouts (Huang et al., 2011; Son and Min, 2014). To overcome this problem, selector devices, including diodes or transistors, are added to RRAM cells to control sneak currents and enhance the reliability of the array (Linn et al., 2010; Yin Chee et al., 2022).
2.1.3 3D RRAM structure
3D RRAM structures (Figure 1c) utilize vertical space in the three dimensions to construct multilayer memory arrays and increase storage capacity with a reduced footprint (Deng et al., 2013). Its implementations can be broadly categorized into (i) Horizontal Stacked 3D RRAM (HRRAM) and (ii) vertical (VRRAM) schemes, which are distinguished to scalability and fabrication complexity (Kim and Li, 2020).
HRRAM is a memory architecture composed of multiple planar 2D RRAM layers superimposed as shown in Figure 1cII. (Chen et al., 2021; Wang et al., 2015). Wang et al. fabricated a 3D RRAM cube by strategically stacking a 2D
The conventional horizontal crossbar array structure is vertically extended for 3D VRRAM (Figure 1cIII; Chen et al., 2022). It is organized in two configurations: The plane word line (WL) structure and the even/odd WL structure (Kim and Li, 2020). It shows increased storage capacity with a small footprint (Kim and Li, 2020; Yue et al., 2014). In particular, compared to 2D crossbar array, 3D VRRAM with an even/odd WL structure occupies only
Structural innovations introduced in these devices give rise to next-generation memory solutions in diverse computing applications (Ielmini and Wong, 2018).
2.2 Nano materials in RRAM structures
Nanomaterials thanks to their properties at the nanoscale, have been revolutionizing modern science and technology. Unlike their rival bulks, Nano-materials’s size and shape determine their unique optical, chemical, thermal, electrical, mechanical, and catalytic properties (Mekuye and Abera, 2023). Nanomaterials are generally known for their dimensional configuration diversification ranging between 1 and 100 nm (Baig et al., 2021). These invaluable features pave the way for emergent fields of applications citing, energy storage and conversion (Yu et al., 2024), environment pollutants degradation (Saroha et al., 2023), disease diagnosis and drug delivery (Karnwal et al., 2024; Singh et al., 2022; Singh et al., 2023), as well as memory architectures like RRAMs (Xu and Liu, 2024). In general, nanomaterials are important for RRAMs’ operational characteristics (Xiao et al., 2023). Indicatively, various memristive metrics such as resistive switching behaviour, ON/OFF ratio, set/reset voltages, retention time, endurance and power consumption (Zayer Fakhreddine et al., 2019) are directly linked to the RRAM nanomaterial properties, namely, the electrical, physical, thermal and mechanical properties (Dongale et al., 2016; Shen et al., 2020; Das et al., 2023). Hence, it is essential to study the different memristive materials dimensional configuration categories (0D, 1D, 2D) (Panisilvam et al., 2024). Numerous nano-materials can be employed for the fabrication of RRAMs, the most common ones are metal oxides, perovskites, and organic materials. Furthermore, other nanomaterials contribute to RRAM modelling, for instance, transition metal oxides (TMOs), and transition metal dichalcogenides (TMDs), etc. Table A1 of the appendix and Figures 1d–f outline the categories of nanomaterials used in RRAMs according to their dimensions.
2.2.1 0D nano-materials in RRAMs
0D carbon nano-materials like carbon nano-dots, graphene quantum dots, and fullerenes assist in memristive functional layer growth through the generation of directional conductive filament formation (Yang Fan et al., 2024). Furthermore, metallic nanoparticles (MNPs) like copper
2.2.2 1D nano-materials in RRAMs
1D nano-materials, thanks to their nanoscale size, unique transport and high electric conductivity at low voltage, are widely used in RRAMs (Zhang et al., 2023). For instance, 1D carbon nano-materials are proven to be good candidates for elaborating highly performing RRAMs because of device stability and weight tunability (Patil et al., 2024). Moreover, metal oxide nanowires and nanorods, such as zinc oxide
2.2.3 2D nano-materials in RRAMs
2D nano-materials are known for ultra-thin, flexible, and layered structures (Muqeet Rehman et al., 2020). 2D TMDs semiconductors such as tungsten disulfide
3 3D RRAM for in-memory computing for biomedical applications
3D RRAM-based systems provide better performance and energy efficiency than traditional computing architectures by computing directly within the memory array (Yan et al., 2019). Several 3D RRAM-based in-memory computing architectures have been proposed and demonstrated in recent years. Wang et al. propose a 3D RRAM-based processing-in-memory (PIM) architecture (Figure 1g) that integrates 3D RRAM with CMOS logic, enabling in situ computation with reduced data movement and energy (Wang Hongzhe et al., 2023). For heavy-load convolutional neural network (CNN) algorithms, the RRAM PIM generally outperforms other architectures in terms of identification rate, speed, and image size (Burr et al., 2017). Building on this, Yao et al. developed a 3D RRAM-based neuromorphic computing system for more efficient processing and analyzing of large-scale datasets, such as images and time series. This system achieved a classification accuracy of 97.8% on the Modified National Institute of Standards and Technology (MNIST) dataset (Peng et al., 2020). Hao et al. presented a study of computing-in-memory macro based on 3D RRAM in 2022. Their work was based on a 3D VRRAM in which they introduced a two-kilobit non-volatile computing-in-memory (nvCIM) macro. They demonstrated an energy efficiency of 8.32 tera-operations per second per watt on 3D vector-matrix multiplication operations. This advancement can facilitate more efficient management of medical imaging data, improving diagnostic accuracy (Wang Tian-Yu et al., 2021). It was found that the brain Magnetic Resonance Imaging (MRI) edge detection performance and the inference accuracy on the Canadian Institute for Advanced Research (CIFAR-10) dataset were improved compared to conventional approaches (Wang Tian-Yu et al., 2021). In 2023, Li et al. recently introduced the monolithic three-dimensional integration of hybrid memory architecture based on RRAM, named M3D-LIME. This M3D-LIME chip (Figure 1h) demonstrated approximately 96% accuracy on the Omniglot dataset with 18.3
4 3D RRAM for multi-sensing arrays for biomedical applications
RRAM architectures can be integrated as chemical signal sensors for disease biomarker detection (Bouzouita et al., 2024) and drug delivery and electrical signal sensors for human multi-sensory perception (Tzouvadaki et al., 2023). Indeed, thanks to their efficient ON/OFF ratio, fast response, low power requirements, low cost, etc. RRAMs can be implemented as highly performing bio/chemical sensing arrays (Tzouvadaki et al., 2020b). In this case, each RRAM layer or section is transformed into a single sensing device, leveraging advancements in memristive nano-materials and biomedical technologies, like lab-on-chip perspectives to simultaneously harvesting multiple responses. In 2018, Adeyemo et al. simulated the detection of three different gases with an 8 × 8 memristive crossbar array. The simulation results highlighted a ten-times increase in accuracy compared to single biosensing devices (Adeyemo et al., 2018). In 2020, Ioulia Tzouvadaki et al. demonstrated a chemical biosensor array (Figure 1i) directly transducing Prostate Specific Antigen (PSA) cancer biomarker concentration levels to discrete memory states, expressing a device-level calibration of the sensing array baseline (Tzouvadaki et al., 2020a). Moreover, in 2023, Doowon Lee et al. developed a highly accurate zirconium nitride
5 Challenges and future directions
5.1 Challenges of 3D RRAM technologies
While memristive crossbar arrays offer considerable potential in the biomedical field, they face numerous obstacles that could impact their development and implementation. While advancing RRAMs, certain critical barriers have emerged in both vertical and horizontal directions. In their work, Gina C. Adam et al. reported potential challenges of memristive matrix integration (Figures 1k, l) citing variability, density, latency, and technology feasibility (Adam et al., 2018). For instance, as RRAM arrays simulate processing operations, errors may appear due to large device variability. Indeed, memristive crossbar arrays often exhibit vast variability due to stoichiometric inconsistency of inharmonious temporal memristive dimensions. Specifically, the variability of selected cross-points within the RRAM causes signal decay (Chen and Lin, 2011). A.P. James et al. emphasized that the variability problem originates either from device-to-device variations, or the nonlinearity of latency and programming. In memristive crossbars, the current flowing through the metal wires (Figure 1k) causes the reduction of voltage drop through the structure. This problem induces the damage of metal wires. The elevation of resistance is a further challenge causing thermal crosstalk and energy loss. All these circumstances initiate signal integrity obstacles (Li et al., 2021; Xu et al., 2015). In general, RRAM challenges reflect on the correct functioning of memristive biosensing devices, therapeutic systems and neural prosthetics since these applications necessitate specific performance metrics citing the response time, sensitivity, stability, reliability, etc., which are linked originally to used memristive device and nanomaterials (Yang Yulong et al., 2024; Zhu et al., 2024).
Various works have been addressing RRAM challenges and proposing solutions. Several nanomaterials have shown a potential to solve some remaining RRAM bottlenecks. For example, it is proved that titanium nitride, while deposited as an atomic layer (ALD-TiN), reduces the penetration of titanium (Ti) atoms to hafnium oxide
5.2 Future directions
There are several significant research opportunities in the future for the 3D RRAM nanomaterial memristive architectures for biomedical applications. Among these, the interconnecting and evolving of new nanomaterials and 2D materials with CIM sensing, and computing properties are paramount. Materials like TMDs, graphene and metal-organic frameworks (MOFs) (Figure 1m) bear the pre-scientific potential to improve the device performance in high-density, multifunctional biomedical implants, diagnostics and real-time health monitoring devices (Zahoor et al., 2023). Specifically, MOFs, with their high surface area and tunable porosity, improve the switching behaviour of RRAM devices through increased ion mobility and low energy consumption. This makes them suitable for neuromorphic systems in implantation devices thus making them potential implant sources (Zhang et al., 2022; Li et al., 2024). Improving these materials to get the best switching characteristics is very important for protracted implantable devices (Shen et al., 2020). However, reducing device variability is an issue that has not yet been solved successfully. More investigations should be dedicated to the reliable suppression of device-to-device and cycle-to-cycle fluctuations to promote the stability of neuromorphic designs for neural computing: sensory signal processing in real-time, control of prosthetic devices, etc., (Shen et al., 2020; Donati and Valle, 2024; Buccelli et al., 2019). The bio-electronic interface is another area with considerable potential for advancement. Therefore, it is desirable that future studies must further extend efforts in stable and long-lasting operation of these interfaces in physiological conditions for the further effectiveness of implantable devices (Zahoor et al., 2023). Recent research on combining RRAM with CMOS chips has shown that on-chip processing is possible at an energy density close to 10 fJ/operation - a critical feature for implantable biomedical applications (Shen et al., 2020). Prospective work should focus on how to continue driving energy consumption down while, at the same time, enhancing the computational abilities of the end-devices, so that in the future it can be possible to perform local analysis and decision making of the data (Li Yijun et al., 2023; D’Agostino et al., 2024; Huo et al., 2022; Aziza et al., 2021). Furthermore, identifying ways in which 3D RRAM devices may be applied for closed-loop therapeutic systems, personalized healthcare, and AI-based diagnosis is another interesting research direction. Additionally, when combined with other new technologies like flexible electronics and biodegradable materials the 3D RRAM may revolutionize the wireless implantable and wearable medicine (Zahoor et al., 2023). Overall, these aspects can open new paths for the use of 3D RRAM nanomaterial memristive architectures in biomedical applications, significantly benefiting healthcare.
Author contributions
MB: Writing – original draft, Writing – review and editing. SP: Writing – original draft, Writing – review and editing. FZ: Supervision, Validation, Writing – review and editing. HB: Supervision, Validation, Writing – review and editing. IT: Conceptualization, Investigation, Methodology, Project administration, Supervision, Validation, Writing – review and editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the Bijzonder Onderzoeksfonds under the Grant No. BOF.STG. 2023.0008.01 and the Tunisian Ministry of Higher Education and Scientific Research scholarship “bourse d’alternance” under the reference “2023-BALT-378”.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that Generative AI was used in the creation of this manuscript. Generative AI was used to rewrite some sentences more academically.
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Appendix
TABLE A1. Nano-materials for RRAM applications.
Keywords: nano-materials, memristor, resistive switching, crossbar, in-memory computing, biosensing arrays
Citation: Bouzouita M, Pathak S, Zayer F, Belgacem H and Tzouvadaki I (2025) Advanced memristive architectures based on nanomaterials for biomedical applications: a mini review. Front. Nanotechnol. 7:1558743. doi: 10.3389/fnano.2025.1558743
Received: 10 January 2025; Accepted: 27 March 2025;
Published: 23 April 2025.
Edited by:
Dimitra G. Georgiadou, University of Southampton, United KingdomReviewed by:
Pan Hu, University of Southern California, Los Angeles, United StatesDeependra Kumar Singh, National Institute of Technology Raipur, India
Copyright © 2025 Bouzouita, Pathak, Zayer, Belgacem and Tzouvadaki. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Ioulia Tzouvadaki , aW91bGlhLnR6b3V2YWRha2lAdWdlbnQuYmU=
†These authors have contributed equally to this work