Spiking Neural Network Integrated with Impact Ionization Field-Effect Transistor Neuron and a Ferroelectric Field-Effect Transistor Synapse.

The integration of artificial spiking neurons based on steep-switching logic devices and artificial synapses with neuromorphic functions enables an energy-efficient computer architecture that mimics the human brain well, known as a spiking neural network (SNN). 2D materials with impact ionization or ferroelectric characteristics have the potential for use in such devices. However, research on 2D spiking neurons remains limited and investigations of 2D artificial synapses far more common.

A nonconjugated radical polymer enables bimodal memory and in-sensor computing operation.

This study reports intrinsic multimodal memristivity of a nonconjugated radical polymer with ambient stability. Organic memristive devices represent powerful candidates for biorealistic data storage and processing. However, there exists a substantial knowledge gap in realizing the synthetic biorealistic systems capable of effectively emulating the cooperative and multimodal activation processes in biological systems.

Versatile Papertronics: Photo-Induced Synapse and Security Applications on Papers.

Paper is a readily available material in nature. Its recyclability, eco-friendliness, portability, flexibility, and affordability make it a favored substrate for researchers seeking cost-effective solutions. Electronic devices based on solution process are fabricated on paper and banknotes using PVK and SnO nanoparticles.

Strengthening Multi-Factor Authentication Through Physically Unclonable Functions in PVDF-HFP-Phase-Dependent a-IGZO Thin-Film Transistors.

For enhanced security in hardware-based security devices, it is essential to extract various independent characteristics from a single device to generate multiple keys based on specific values. Additionally, the secure destruction of authentication information is crucial for the integrity of the data. Doped amorphous indium gallium zinc oxide (a-IGZO) thin-film transistors (TFTs) using poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) induce a dipole doping effect through a phase-transition process, creating physically unclonable function (PUF) devices for secure user information protection.

Imitating Synapse Behavior: Exploiting Off-Current in TPBi-Doped Small Molecule Phototransistors for Broadband Wavelength Recognition.

Phototransistors have gained significant attention in diverse applications such as photodetectors, image sensors, and neuromorphic devices due to their ability to control electrical characteristics through photoresponse. The choice of photoactive materials in phototransistor research significantly impacts its development. In this study, we propose a novel device that emulates artificial synaptic behavior by leveraging the off-current of a phototransistor.

Machine Learning Attacks-Resistant Security by Mixed-Assembled Layers-Inserted Graphene Physically Unclonable Function.

Mixed layers of octadecyltrichlorosilane (ODTS) and 1H,1H,2H,2H-perfluorooctyltriethoxysilane (FOTS) on an active layer of graphene are used to induce a disordered doping state and form a robust defense system against machine-learning attacks (ML attacks). The resulting security key is formed from a 12 × 12 array of currents produced at a low voltage of 100 mV. The uniformity and inter-Hamming distance (HD) of the security key are 50.

Double-Floating-Gate van der Waals Transistor for High-Precision Synaptic Operations.

Two-dimensional materials and their heterostructures have thus far been identified as leading candidates for nanoelectronics owing to the near-atom thickness, superior electrostatic control, and adjustable device architecture. These characteristics are indeed advantageous for neuro-inspired computing hardware where precise programming is strongly required. However, its successful demonstration fully utilizing all of the given benefits remains to be further developed.

Imidazole-based artificial synapses for neuromorphic computing: a cluster-type conductive filament controllable nanocluster nucleation.

Memristive synapses based on conductive bridging RAMs (CBRAMs) utilize a switching layer having low binding energy with active metals for excellent analog conductance modulation, but the resulting unstable conductive filaments cause fluctuation and drift of the conductance. This tunability-stability dilemma makes it difficult to implement practical neuromorphic computing. A novel method is proposed to enhance the stability and controllability of conductive filaments by introducing imidazole groups that boost the nucleation of Cu nanoclusters in the ultrathin polymer switching layer through the initiated chemical vapor deposition (iCVD) process.

Amorphous ITZO-Based Selector Device for Memristor Crossbar Array.

In the era of digital transformation, a memristor and memristive circuit can provide an advanced computer architecture that efficiently processes a vast quantity of data. With the unique characteristic of memristor, a memristive crossbar array has been utilized for realization of nonvolatile memory, logic-in-memory circuit, and neuromorphic system. However, the crossbar array architecture suffers from leakage of current, known as the sneak current, which causes a cross-talk interference problem between adjacent memristor devices, leading to an unavoidable operational error and high power consumption.

Preventing Vanishing Gradient Problem of Hardware Neuromorphic System by Implementing Imidazole-Based Memristive ReLU Activation Neuron.

With advances in artificial intelligent services, brain-inspired neuromorphic systems with synaptic devices are recently attracting significant interest to circumvent the von Neumann bottleneck. However, the increasing trend of deep neural network parameters causes huge power consumption and large area overhead of a nonlinear neuron electronic circuit, and it incurs a vanishing gradient problem. Here, a memristor-based compact and energy-efficient neuron device is presented to implement a rectifying linear unit (ReLU) activation function.

Graphene Oxide-Based Memristive Logic-in-Memory Circuit Enabling Normally-Off Computing.

Memristive logic-in-memory circuits can provide energy- and cost-efficient computing, which is essential for artificial intelligence-based applications in the coming Internet-of-things era. Although memristive logic-in-memory circuits have been previously reported, the logic architecture requiring additional components and the non-uniform switching of memristor have restricted demonstrations to simple gates. Using a nanoscale graphene oxide (GO) nanosheets-based memristor, we demonstrate the feasibility of a non-volatile logic-in-memory circuit that enables normally-off in-memory computing.

Hardware-Intrinsic Physical Unclonable Functions by Harnessing Nonlinear Conductance Variation in Oxide Semiconductor-Based Diode.

With the advancement of the Internet of Things (IoT), numerous electronic devices are connected to each other and exchange a vast amount of data via the Internet. As the number of connected devices increases, security concerns have become more significant. As one of the potential solutions for security issues, hardware intrinsic physical unclonable functions (PUFs) are emerging semiconductor devices that exploit inherent randomness generated during the manufacturing process.

Disordered Mixture of Self-Assembled Molecular Functional Groups on Heterointerfaces with p-Si Leads to Multiple Key Generation in Physical Unclonable Functions.

Physical unclonable function (PUF) security devices based on hardware are becoming an effective strategy to overcome the dependency of the internet cloud and software-based hacking vulnerabilities. On the other hand, existing Si-based artificial security devices have several issues, including the absence of a method for multiple key generation, complex and expensive fabrication processes, and easy prediction compared to devices retaining natural randomness. Herein, to generate unique and unpredictable multiple security keys, this paper proposes novel PUF devices consisting of a disordered random mixture of two self-assembled monolayers (SAMs) formed onto p-type Si.

Conductive-bridging random-access memories for emerging neuromorphic computing.

With the increasing utilisation of artificial intelligence, there is a renewed demand for the development of novel neuromorphic computing owing to the drawbacks of the existing computing paradigm based on the von Neumann architecture. Extensive studies have been performed on memristors as their electrical nature is similar to those of biological synapses and neurons. However, most hardware-based artificial neural networks (ANNs) have been developed with oxide-based memristors owing to their high compatibility with mature complementary metal-oxide-semiconductor (CMOS) processes.

Polymer Analog Memristive Synapse with Atomic-Scale Conductive Filament for Flexible Neuromorphic Computing System.

With the advent of artificial intelligence (AI), memristors have received significant interest as a synaptic building block for neuromorphic systems, where each synaptic memristor should operate in an analog fashion, exhibiting multilevel accessible conductance states. Here, we demonstrate that the transition of the operation mode in poly(1,3,5-trivinyl-1,3,5-trimethyl cyclotrisiloxane) (pV3D3)-based flexible memristor from conventional binary to synaptic analog switching can be achieved simply by reducing the size of the formed filament. With the quantized conductance states observed in the flexible pV3D3 memristor, analog potentiation and depression characteristics of the memristive synapse are obtained through the growth of atomically thin Cu filament and lateral dissolution of the filament via dominant electric field effect, respectively.

Functional Circuitry on Commercial Fabric via Textile-Compatible Nanoscale Film Coating Process for Fibertronics.

Fabric-based electronic textiles (e-textiles) are the fundamental components of wearable electronic systems, which can provide convenient hand-free access to computer and electronics applications. However, e-textile technologies presently face significant technical challenges. These challenges include difficulties of fabrication due to the delicate nature of the materials, and limited operating time, a consequence of the conventional normally on computing architecture, with volatile power-hungry electronic components, and modest battery storage.

Flexible Nonvolatile Polymer Memory Array on Plastic Substrate via Initiated Chemical Vapor Deposition.

Resistive random access memory based on polymer thin films has been developed as a promising flexible nonvolatile memory for flexible electronic systems. Memory plays an important role in all modern electronic systems for data storage, processing, and communication; thus, the development of flexible memory is essential for the realization of flexible electronics. However, the existing solution-processed, polymer-based RRAMs have exhibited serious drawbacks in terms of the uniformity, electrical stability, and long-term stability of the polymer thin films.

Prevention of tissue injury by ribbon antisense to TGF-beta1 in the kidney.

Transforming growth factor-beta1 (TGF-beta1) is an important mediator of glomerulosclerosis and tubulointerstitial fibrosis in renal diseases. We designed ribbon-type antisense oligos of TGF-beta1, TGF-beta1 RiAS, and combined them with a short peptide of the nuclear localization signal to form a transfection complex of DNA/peptide/liposomes (DPL) for enhanced cellular uptake. When H4IIE cells were transfected with TGF-beta1 RiAS, the level of TGF-beta1 mRNA was reduced by >70%.