Optimization of Memristive Crossbar Array for Physical Unclonable Function Applications

Khan, Muhammad Ibrar; Ali, Shawkat; Ikram, Ataul Aziz; Bermak, Amine

doi:10.1109/access.2021.3087810

Cited by 7 publications

(4 citation statements)

References 47 publications

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“…The auxiliary circuit and programmed algorithms could be introduced to address the non-idealities issue in memristor crossbar array. [52,53] Specifically, the physiological and environmental signals collected by sensors were directly processed by textile-type memristors and visually fed back through textile display, thus offering an effective and comfortable interactive tool. For instance, we fabricated an ultraviolet-light-monitoringfeedback cloth by weaving together textile-type memristors, fiber sensors for ultraviolet light detection and textile display (Figure 5b,c; Figure S34a-d, Supporting Information).…”

Section: Resultsmentioning

confidence: 99%

Highly Reliable Textile‐Type Memristor by Designing Aligned Nanochannels

et al. 2023

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Information‐processing devices are the core components of modern electronics. Integrating them into textiles is the indispensable demand for electronic textiles to form close‐loop functional systems. Memristors with crossbar configuration are regarded as promising building blocks to design woven information‐processing devices that seamlessly unify with textiles. However, the memristors always suffer from severe temporal and spatial variations due to the random growth of conductive filaments during filamentary switching processes. Here, inspired by the ion nanochannels across synaptic membranes, a highly reliable textile‐type memristor made of Pt/CuZnS memristive fiber with aligned nanochannels, showing small set voltage variation (<5.6%) under ultralow set voltage (≈0.089 V), high on/off ratio (≈106), and low power consumption (0.1 nW), is reported. Experimental evidence indicate that nanochannels with abundant active S defects can anchor silver ions and confine their migrations to form orderly and efficient conductive filaments. Such memristive performances enable the resultant textile‐type memristor array to have high device‐to‐device uniformity and process complex physiological data like brainwave signals with high recognition accuracy (95%). The textile‐type memristor arrays are mechanically durable to withstand hundreds of bending and sliding deformations, and seamlessly unified with sensing, power‐supplying, and displaying textiles/fibers to form all‐textile integrated electronic systems for new generation human‐machine interactions.

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Section: Resultsmentioning

confidence: 99%

Highly Reliable Textile‐Type Memristor by Designing Aligned Nanochannels

et al. 2023

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“…The majority of memory-based cryptographic techniques for hardware security are based on physical unclonable functions (PUFs) (James, 2019 ). A large number of memory-crossbar-based PUFs have been proposed in the literature, for example, metal-oxide memristor-based or RRAM (Rose and Meade, 2015 ; Yansong et al, 2015 ; Uddin et al, 2017 ; Khan et al, 2021 ; Kim et al, 2021 ) etc. The PUF methods use variations in device parameters such as resistance state, switching time, and threshold voltages.…”

Section: Graphene-based Rram Applicationsmentioning

confidence: 99%

Graphene-based RRAM devices for neural computing

Das,

Reghuvaran

et al. 2023

Front. Neurosci.

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Resistive random access memory is very well known for its potential application in in-memory and neural computing. However, they often have different types of device-to-device and cycle-to-cycle variability. This makes it harder to build highly accurate crossbar arrays. Traditional RRAM designs make use of various filament-based oxide materials for creating a channel that is sandwiched between two electrodes to form a two-terminal structure. They are often subjected to mechanical and electrical stress over repeated read-and-write cycles. The behavior of these devices often varies in practice across wafer arrays over these stresses when fabricated. The use of emerging 2D materials is explored to improve electrical endurance, long retention time, high switching speed, and fewer power losses. This study provides an in-depth exploration of neuro-memristive computing and its potential applications, focusing specifically on the utilization of graphene and 2D materials in RRAM for neural computing. The study presents a comprehensive analysis of the structural and design aspects of graphene-based RRAM, along with a thorough examination of commercially available RRAM models and their fabrication techniques. Furthermore, the study investigates the diverse range of applications that can benefit from graphene-based RRAM devices.

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“…This is used to improve system lifetime in mapping machine learning workloads. The work in [33] focused on the optimization of Memristive Crossbar Arrays. In [34], the authors investigated memristance variations capture synaptic weight variations.…”

Section: B Neuromorphic State Machine (Nsm)mentioning

confidence: 99%

ASM-ROBOT: A Cyber-Physical Home Automation Controller with Memristive Reconfigurable State Machine

Okafor¹,

Longe²

2022

IJACSA

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In the next 5 to 10 years, digital Artificial Intelligence with Machine Circuit Learning Algorithms (MCLA) will become the mainstream in complex automated robots. Its power concerns, ethical perspectives, including the issues of digital sensing, actuation, mobility, efficient processcomputation, and wireless communication will require advanced neuromorphic process variable controls. Existing home automated robots lack memristic associative memory. This work presents Cyber-Physical Home Automation System (CPHAS) using Memristive Reconfigurable Algorithmic State Machine (MRASM) chart. A process control architecture that supports Concurrent Wireless Data Streams and Power-Transfer (CWDSPT) is developed. Unlike legacy systems with powersplitting (PS) and time-switching (TS) controls, the MRASM-ROBOT explores granular wireless signal controls through unmodulated high-power continuous wave (CW). This transmits infinite process variables using Orthogonal Space-Time Block Code (OSTBC) for interference reduction. The CWDSPT transmitter and receiver circuit design for signal processing are implemented with complexity noise-error reduction during telemetry data decoding. Received signals are error-buffered while gathering control variables' status.Transceiver Memristive neuromorphic circuits are introduced for computational acceleration in the design. Hardware circuit design is tested for system reliability considering the derived schematic models for all process variables. Under small range space diversity, the system demonstrated significant memory stabilization at the synchronous iteration of the synaptic circuitry.

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Optimization of Memristive Crossbar Array for Physical Unclonable Function Applications

Cited by 7 publications

References 47 publications

Highly Reliable Textile‐Type Memristor by Designing Aligned Nanochannels

Highly Reliable Textile‐Type Memristor by Designing Aligned Nanochannels

Graphene-based RRAM devices for neural computing

ASM-ROBOT: A Cyber-Physical Home Automation Controller with Memristive Reconfigurable State Machine

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