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. The face classification capability of our memristor is evaluated via simulation using an artificial neural network consisting of pV3D3 memristor synapses. These results will encourage the development of soft neuromorphic intelligent systems.

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. Here, we report a novel poly(ethylene glycol dimethacrylate) (pEGDMA)-textile memristive nonvolatile logic-in-memory circuit, enabling normally off computing, that can overcome those challenges. To form the metal electrode and resistive switching layer, strands of cotton yarn were coated with aluminum (Al) using a solution dip coating method, and the pEGDMA was conformally applied using an initiated chemical vapor deposition process. The intersection of two Al/pEGDMA coated yarns becomes a unit memristor in the lattice structure. The pEGDMA-Textile Memristor (ETM), a form of crossbar array, was interwoven using a grid of Al/pEGDMA coated yarns and untreated yarns. The former were employed in the active memristor and the latter suppressed cell-to-cell disturbance. We experimentally demonstrated for the first time that the basic Boolean functions, including a half adder as well as NOT, NOR, OR, AND, and NAND logic gates, are successfully implemented with the ETM crossbar array on a fabric substrate. This research may represent a breakthrough development for practical wearable and smart fibertronics.

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. The PUF security key, generated at VGS = 20 V in a 20 × 10 grid, demonstrates uniformity of 42% and inter-Hamming distance (inter-HD) of 49.79% in the ß-phase of PVDF-HFP. However, in the γ-phase, the uniformity drops to 22.5%, and inter-HD decreases to 35.74%, indicating potential security key destruction during the phase transition. To enhance security, a multi-factor authentication (MFA) system is integrated, utilizing five security keys extracted from various TFT parameters. The security keys from turn-on voltage (VON), VGS = 20 V, VGS = 30 V, mobility, and threshold voltage (Vth) exhibit near-ideal uniformities and inter-HDs, with the highest values of 58% and 51.68%, respectively. The dual security system, combining phase transition and MFA, establishes a robust protection mechanism for privacy-sensitive user information.

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 were fabricated on paper and banknotes using PVK and SnO2 nanoparticles. The devices manufactured on paper substrates exhibited photosynaptic behavior under ultraviolet pulse illumination, stemming from numerous interactions on the surface of the SnO2 nanoparticles. A light-modulated artificial synapse device was realized on a paper at a low voltage bias of -0.01 V, with an average recognition rate of 91.7% based on the Yale Face Database. As a security device on a banknote, 400 devices in a 20 × 20 array configuration exhibited random electrical characteristics owing to the local morphology of the SnO2 nanoparticles and differences in the depletion layer width at the SnO2/PVK interface. The security PUF key based on the current distribution extracted at -1 V showed unpredictable reproducibility with 50% uniformity, 48.7% inter-Hamming distance, and 50.1% bit-aliasing rates. Moreover, the device maintained its properties for more than 210 d under a curvature radius of 8.75 mm and bias and UV irradiation stress conditions. This article is protected by copyright. All rights reserved.

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. We utilize a p-type organic semiconductor, dinaphtho[2,3-b:2',3'- f]thieno[3,2-b]thiophene (DNTT), as the channel material and dope it with the organic semiconductor 2,2',2″-(1,3,5-benzinetriyl)-tris(1-phenyl-1-H-benzimidazole) (TPBi) on the DNTT transistor. Under light illumination, the general DNTT transistor shows no change in off-current, except at 400 nm wavelength, whereas the TPBi-doped DNTT phototransistor exhibits increased off-current across all wavelength bands. Notably, DNTT phototransistors demonstrate broad photoresponse characteristics in the wavelength range of 400-1000 nm. We successfully simulate artificial synaptic behavior by differentiating the level of off-current and achieving a recognition rate of over 70% across all wavelength bands.

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. The unclonable security key generated from PUF can address the inherent limitations of conventional electronic systems which depend solely on software. Although numerous PUFs based on the emerging memory devices requiring switching operations have been proposed, achieving hardware intrinsic PUF with low power consumption remains a key challenge. Here, we demonstrate that the process-induced nonlinear conductance variations of oxide semiconductor-based Schottky diodes provide a suitable source of entropy for the implementation of PUF without switching operation. Using a mild oxygen plasma treatment, the surface electron accumulation layer that forms naturally in oxide semiconductor film can be partially eliminated, resulting in a large variation of nonlinearity as an exotic entropy source. The mild plasma-treated Schottky diodes showed near ideal 50% average uniformity and uniqueness, as well as an ideal entropy value without the need for additional hardware area and power costs. These findings will pave the way for the development of hardware intrinsic PUFs to realize energy-efficient cryptographic hardware.

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. The memristor based on GO film with an abundance of unusual functional groups exhibited unipolar resistive switching behavior with reliable endurance and retention characteristics, making it suitable for logic-in-memory circuit application. In a state of low resistance, temperature-dependent resistance and I-V characteristics indicated the presence of a metallic Ni filament. Using memristor-aided logic (MAGIC) architecture, we performed NOT and NOR gates experimentally. Additionally, other logic gates such as AND, NAND, and OR were successfully implemented by combining NOT and NOR universal logic gates in a crossbar array. These findings will pave the way for the development of next-generation computer systems beyond the von Neumann architecture, as well as carbon-based nanoelectronics in the future.

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. Here, we present an amorphous In-Sn-Zn-O (a-ITZO) oxide semiconductor-based selector device to address the sneak current issue. The a-ITZO-selector device is realized with the back-to-back Schottky diode with nonlinear current-voltage (I-V) characteristics. Its nonlinearity is dependent on the oxygen plasma treatment process which can suppress the surface electron accumulation layer arising on the a-ITZO surface. The a-ITZO-selector device shows reliable characteristics against electrical stress and high temperature. In addition, the selector device allows for a stable read margin over 1 Mbit of memristor crossbar array. The findings may offer a feasible solution for the development of a high-density memristor crossbar array.

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. The proposed PUF devices exhibited multikeys at different voltage biasing, including 0 V, through the arbitrary dipole effect. As a result, multiple unpredictable hardware security keys were generated from one device using a simple solution-coating process. The PUF security device based on the mixture of materials with different dipoles developed in this study can provide valuable insights for implementing various PUF devices in the future.

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 50.0 ± 12.3% and 45.5 ± 16.7%, respectively, indicating higher security performance than other graphene-based security keys. Raman spectroscopy confirmed the uniqueness of the 10,000 points, with the degree of shift of the G peak distinguishing the number of carriers. The resulting defense system has a 10.33% ML attack accuracy, while a FOTS-inserted graphene device is easily predictable with a 44.81% ML attack accuracy.

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. To emulate the volatile and gradual switching of the ReLU function, a copolymer memristor with a hybrid structure is proposed using a copolymer/inorganic bilayer. The functional copolymer film developed by introducing imidazole functional groups enables the formation of nanocluster-type pseudo-conductive filaments by boosting the nucleation of Cu nanoclusters, causing gradual switching. The ReLU neuron device is successfully demonstrated by integrating the memristor with amorphous InGaZnO thin-film transistors, and achieves 0.5 pJ of energy consumption based on sub-10 µA operation current and high-speed switching of 650 ns. Furthermore, device-to-system-level simulation using neuron devices on the MNIST dataset demonstrates that the vanishing gradient problem is effectively resolved by five-layer deep neural networks. The proposed neuron device will enable the implementation of high-density and energy-efficient hardware neuromorphic systems.

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. Herein, we present van der Waals (vdW) integrated synaptic transistors with multistacked floating gates, which are reconfigured upon surface oxidation. When compared with a conventional device structure with a single floating gate, our double-floating-gate (DFG) device exhibits better nonvolatile memory performance, including a large memory window (>100 V), high on-off current ratio (∼107), relatively long retention time (>5000 s), and satisfactory cyclic endurance (>500 cycles), all of which can be attributed to its increased charge-storage capacity and spatial redistribution. This facilitates highly effective modulation of trapped charge density with a large dynamic range. Consequently, the DFG transistor exhibits an improved weight update profile in long-term potentiation/depression synaptic behavior for nearly ideal classification accuracies of up to 96.12% (MNIST) and 81.68% (Fashion-MNIST). Our work adds a powerful option to vdW-bonded device structures for highly efficient neuromorphic computing.

Imidazole-based artificial synapses for neuromorphic computing: a cluster-type conductive filament via 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. It is confirmed that conductive filaments based on nanoclusters with specific gaps are generated in the copolymer medium using this method. Furthermore, by modulating the tunneling gaps, an ultra-wide conductance range of analog tunable conductive filaments is achieved from several hundreds of nS to a few mS with a sub-1 V driving voltage. Through this, both reliable and stable analog switching are achieved with low cycle-to-cycle and device-to-device weight update variations and separable state retention with 32 states. This approach paves the way for the extension of state availability in synaptic devices to overcome the tunability-stability dilemma, which is essential for the synaptic elements in neuromorphic systems.

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. Considering the advantages of conductive-bridging random-access memories (CBRAMs), such as their high scalability, high on-off current with a wide dynamic range, and low off-current, over oxide-based memristors, extensive studies on CBRAMs are required. In this review, the basics of operation of CBRAMs are examined in detail, from the formation of metal nanoclusters to filament bridging. Additionally, state-of-the-art experimental demonstrations of CBRAM-based artificial synapses and neurons are presented. Finally, CBRAM-based ANNs are discussed, including deep neural networks and spiking neural networks, along with other emerging computing applications. This review is expected to pave the way toward further development of large-scale CBRAM array systems.

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. Here, we present poly(1,3,5-trimethyl-1,3,5-trivinyl cyclotrisiloxane) (pV3D3)-based RRAM arrays fabricated via the solvent-free technique called initiated chemical vapor deposition (iCVD) process for flexible memory application. Because of the outstanding chemical stability of pV3D3 films, the pV3D3-RRAM arrays can be fabricated by a conventional photolithography process. The pV3D3-RRAM on flexible substrates showed unipolar resistive switching memory with an on/off ratio of over 10(7), stable retention time for 10(5) s, excellent cycling endurance over 10(5) cycles, and robust immunity to mechanical stress. In addition, pV3D3-RRAMs showed good uniformity in terms of device-to-device distribution. The pV3D3-RRAM will pave the way for development of next-generation flexible nonvolatile memory devices.

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%. We then examined the ratio of the kidney weight per body weight in rats. Whereas the weight ratio was 0.47% for the normal kidney, the ratio was 0.99% on day 5 after unilateral ureteric obstruction (UUO). The ratios were 0.95% with PBS injection, 1.07% with scrambled RiAS, and 0.68% with TGF-beta1 RiAS. When examined for TGF-beta1 expression in the tissue, the level of TGF-beta1 mRNA was also significantly reduced following treatment with TGF-beta1 RiAS. Further, physical changes such as diminished dilation, atrophy, as well as apoptosis caused by UUO were also found to be markedly reduced by TGF-beta1 RiAS. The results show that ribbon antisense to TGF-beta1 when combined with efficient uptake can effectively block TGF-beta1 expression and preserve tissue integrity in kidneys with UUO.