Site‐Specific Emulation of Neuronal Synaptic Behavior in Au Nanoparticle‐Decorated Self‐Organized TiO_x Surface

Abstract

Neuromorphic computing is a potential approach for imitating massive parallel processing capabilities of a bio‐synapse. To date, memristors have emerged as the most appropriate device for designing artificial synapses for this purpose due to their excellent analog switching capacities with high endurance and retention. However, to build an operational neuromorphic platform capable of processing high‐density information, memristive synapses with nanoscale footprint are important, albeit with device size scaled down, retaining analog plasticity and low power requirement often become a challenge. This paper demonstrates site‐selective self‐assembly of Au nanoparticles on a patterned TiOx layer formed as a result of ion‐induced self‐organization, resulting in site‐specific resistive switching and emulation of bio‐synaptic behavior (e.g., potentiation, depression, spike rate‐dependent and spike timing‐dependent plasticity, paired pulse facilitation, and post tetanic potentiation) at nanoscale. The use of local probe‐based methods enables nanoscale probing on the anisotropic films. With the help of various microscopic and spectroscopic analytical tools, the observed results are attributed to defect migration and self‐assembly of implanted Au atoms on self‐organized TiOx surfaces. By leveraging the site‐selective evolution of gold‐nanostructures, the functionalized TiOx surface holds significant potential in a multitude of fields for developing cutting‐edge neuromorphic computing platforms and Au‐based biosensors with high‐density integration.

Memfractance of Proteinoids

“…Compared to aqueous proteinoids, the decreased α 3 coefficient for the current–voltage response curves of proteinoid–HAP suspensions suggests the modulation of higher-order history-dependent electrical conduction effects compared to those of aqueous proteinoid samples alone. While prior studies have related certain I – V polynomial terms to possible resistive memory mechanisms in alternate systems, − sufficient underlying evidence currently lacks in this work to definitively ascribe observed coefficient shifts to changes in specific molecular switching phenomena without further multimodal characterization. This aligns with a transition from pure memfractance toward memristive responses as charge transport through the mineralized proteinoids becomes less transient.…”

Compliance-Free and Forming-Free Digital and Analog Resistive Switching in a Perovskite-Based Artificial Synaptic Device: Mimicking Classical Pavlovian Learning

“…10 In addition, the resistive switching (RS) behavior is commonly observed in two-terminal memory devices such as memristors. 24 Recently, noteworthy development has been observed in the memristor technology as a next-generation nonvolatile memory application because of its structural simplicity [metal/semiconductor (insulator)/metal], energyefficient operation, and faster operational speeds. 28 The RS behavior in memristive devices is classified into digital RS (DRS) and analog RS (ARS) behaviors.…”

“…Recently, researchers have been intensively working on designing novel electronic devices that not only mimic the behavior of the biological brain but also address the current limitations of the renowned von Neumann architecture and conventional CMOS technology. Therefore, the new computing architecture, termed neuromorphic computing (NC), is a highly promising and demanding concept in the advanced computing framework. − The NC is a computing architecture where artificial synapses and neurons are integrated in a compact manner, following the topology of neural networks, to mimic neuronal and synaptic computations efficiently. , Also, this architecture is exceptionally energy efficient, consumes less power, and enables very high-speed data processing. , The human brain can perform advanced computing tasks by undergoing massive parallel processing of information with extremely low power consumption. − Also, the human brain comprises a vast number of neurons (∼10 11 ) and synapses (∼10 15 ) that form a complex bioneural network to perform everyday tasks of intricate complexity. , In the human brain, electrical stimuli excite all neurons, facilitating the reception and transmission of information through a combination of electrical and chemical signals across synapses. − In today’s context, memristors with bionic functions are drawing considerable attention, as they successfully simulate different synaptic features. ,,,− This development opens doors for utilizing memristors in NC applications. ,,− Recently, there has been substantial progress in semiconductor circuits designed to emulate biobrain functionalities, − with memristors serving as synapses and arithmetic logic units as neurons. ,− …”