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Artificial intelligence (AI) integrated circuits (IC) have memory devices as the key component. Due to more complex algorithms and architectures required by neuroscience and other medical applications, various memory structures have been widely proposed and investigated by involving nanomaterials, such as memristors. Due to reliability issues of mass production, the dominant memory devices in many computers are still dynamic random access memory (DRAM). A DRAM has one transistor and one capacitor, and so it contains two devices and requires a more compact design to replace. A one-transistor memory device which is more compact than DRAM is proposed. As far as the authors know, this is the first/novel flexible and transparent one-transistor memory device without any additional process to make a typical transistor and which is based on polyvinyl alcohol. By using indium-titanium-oxide (ITO) as the metal gate, PVA as the dielectric layer and In-Ga-Zn-O (IGZO) as the channel, the memory is implemented mainly based on amorphous oxides and transparent flexible nanomaterials. The charge storage for the memory function was investigated here and is attributed to the mechanism of charge trapping between the ITO/IGZO junctions. It shows typical artificial synaptic transmission behaviors such as EPSC (excitatory postsynaptic currents). Such a first flexible and transparent one-transistor memory device based on PVA has one capacitor less than DRAM and could be a potential and promising candidate as an alternative for DRAM, especially in the highly complex AI chips needed for numerous medical applications. The flexible memory nanodevice based on flexible dielectrics such as PVA, which shows typical memory and artificial synaptic behaviors, could also be suitable for portable, flexible, transparent or skin-like medical applications.
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http://dx.doi.org/10.2147/IJN.S200581 | DOI Listing |
Nano Lett
May 2025
MIIT Key Laboratory of Advanced Display Materials and Devices, School of Materials Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China.
Two-dimensional (2D) layered BiSeO, a novel high-k oxide material, has shown considerable potential for enhancing memristor performance. In this study, high-crystallinity 2D BiSeO nanosheets were successfully exfoliated, demonstrating that oxygen-vacancy-induced BiSeO memristors exhibit superior nonvolatile characteristics. Specifically, these memristors exhibit an ultrahigh on/off ratio (up to 10), an extremely low off-state current (10 A), and rapid switching speeds (160 ns for SET and 110 ns for RESET).
View Article and Find Full Text PDFACS Nano
April 2025
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.
Emerging applications in data-intensive computing and circuit security demand logic circuits with high functional density, reconfigurability, and energy efficiency. Here, we demonstrate nonvolatile reconfigurable four-mode field-effect transistors (NVR4M-FETs) based on two-dimensional (2D) MoTe and CuInPS (CIPS), offering both polarity switching and threshold voltage modulation. The device exploits the ferroelectric polarization of CIPS at the source/drain regions to achieve dynamic control over the transistor polarity, enabling transitions between n-type and p-type states through polarization-induced local electrostatic doping.
View Article and Find Full Text PDFNat Commun
February 2025
Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, China.
Machine learning is the core of artificial intelligence. Using optical signals for training and converting them into electrical signals for inference, combines the strengths of both, and thus can greatly improve machine learning efficiency. Optoelectronic memories are the hardware foundation for this strategy.
View Article and Find Full Text PDFNano Lett
February 2025
Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia.
Two-dimensional-material-based memristor arrays hold promise for data-centric applications such as artificial intelligence and big data. However, accessing individual memristor cells and effectively controlling sneak current paths remain challenging. Here, we propose a van der Waals engineering approach to create one-transistor-one-memristor (1T1M) cells by assembling the emerging two-dimensional ferroelectric CuCrPS with MoS and -BN.
View Article and Find Full Text PDFNano Lett
February 2025
Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea.
Three-dimensional vertically stacked memory is more cost-effective than two-dimensional stacked memory. Vertically stacked memory using ferroelectric materials has great potential not only in high-density memory but also in neuromorphic fields because it secures low voltage and fast operation speed. This paper presents the implementation of a ferroelectric capacitor comprising a vertical two-layer stacked structure composed of a titanium nitride (TiN)/aluminum-doped hafnium oxide/TiN configuration.
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