Experimental demonstration of combination-encoding content-addressable memory of 0.75 bits per switch utilizing Hf-Zr-O ferroelectric tunnel junctions.

We experimentally demonstrate the concept of combination-encoding content-addressable memory (CECAM) that offers much higher content density than any other content-addressable memory devices proposed to date. In this work, CECAM was fabricated and validated with a hafnium-zirconium oxide (HZO) ferroelectric tunnel junction (FTJ) crossbar array. The new CAM structure, which utilizes nonvolatile memory devices, offers numerous advantages including low-current operation (FTJ), standby power reduction (ferroelectric HZO), and increased content density. Multibit data are encoded and stored in multi-switch CECAM. Perfect-match searching in CECAM with a reasonable match current (lower than nA) for different sizes of CECAM has been validated from a novel CAM device. We demonstrate N-CECAM (with keys encoded into 2N-long binary arrays) for N = 3 (using 6 FTJs) and 4 (using 8 FTJs), leading to content densities of 0.667 and 0.75 bits per switch, which highlight 33% and 50% increase in content density compared to that of the conventional TCAM (0.5 bits per switch).

Purely self-rectifying memristor-based passive crossbar array for artificial neural network accelerators.

Memristor-integrated passive crossbar arrays (CAs) could potentially accelerate neural network (NN) computations, but studies on these devices are limited to software-based simulations owing to their poor reliability. Herein, we propose a self-rectifying memristor-based 1 kb CA as a hardware accelerator for NN computations. We conducted fully hardware-based single-layer NN classification tasks involving the Modified National Institute of Standards and Technology database using the developed passive CA, and achieved 100% classification accuracy for 1500 test sets. We also investigated the influences of the defect-tolerance capability of the CA, impact of the conductance range of the integrated memristors, and presence or absence of selection functionality in the integrated memristors on the image classification tasks. We offer valuable insights into the behavior and performance of CA devices under various conditions and provide evidence of the practicality of memristor-integrated passive CAs as hardware accelerators for NN applications.

Surface-Dominated HfO2 Nanorod-Based Memristor Exhibiting Highly Linear and Symmetrical Conductance Modulation for High-Precision Neuromorphic Computing.

The switching characteristics and performance of oxide-based memristors are predominately determined by oxygen- or oxygen-vacancy-mediated redox reactions and the consequent formation of conducting filaments (CFs). Devices using oxide thin films as the switching layer usually require an electroforming process for subsequent switching operations, which induces large device-to-device variations. In addition, the hard-to-control redox reaction during repeated switching causes random fluctuations or degradation of each resistance state, hindering reliable switching operations. In this study, an HfO2 nanorod (NR)-based memristor is proposed for simultaneously achieving highly uniform, electroforming-free, fast, and reliable analogue switching properties. The well-controlled redox reaction due to the easy gas exchange with the environment at the surface of the NRs enhances the generation of oxygen or oxygen vacancies during the switching operation, resulting in electroforming-free and reliable switching behavior. In addition, the one-dimensional surface growth of CFs facilitates highly linear conductance modulation with smaller conductance changes compared with the two-dimensional volume growth in thin-film-based memristors, resulting in a high accuracy of >92% in the Modified National Institute of Standards and Technology pattern-recognition test and desirable spike-timing-dependent plasticity.

Self-rectifying resistive memory in passive crossbar arrays.

Conventional computing architectures are poor suited to the unique workload demands of deep learning, which has led to a surge in interest in memory-centric computing. Herein, a trilayer (Hf0.8Si0.2O2/Al2O3/Hf0.5Si0.5O2)-based self-rectifying resistive memory cell (SRMC) that exhibits (i) large selectivity (ca. 104), (ii) two-bit operation, (iii) low read power (4 and 0.8 nW for low and high resistance states, respectively), (iv) read latency (<10 µs), (v) excellent non-volatility (data retention >104 s at 85 °C), and (vi) complementary metal-oxide-semiconductor compatibility (maximum supply voltage ≤5 V) is introduced, which outperforms previously reported SRMCs. These characteristics render the SRMC highly suitable for the main memory for memory-centric computing which can improve deep learning acceleration. Furthermore, the low programming power (ca. 18 nW), latency (100 µs), and endurance (>106) highlight the energy-efficiency and highly reliable random-access memory of our SRMC. The feasible operation of individual SRMCs in passive crossbar arrays of different sizes (30 × 30, 160 × 160, and 320 × 320) is attributed to the large asymmetry and nonlinearity in the current-voltage behavior of the proposed SRMC, verifying its potential for application in large-scale and high-density non-volatile memory for memory-centric computing.

Novel nano-plasmonic sensing platform based on vertical conductive bridge.

A novel nano-plasmonic sensing platform based on vertical conductive bridge was suggested as an alternative geometry for taking full advantages of unique properties of conductive junction while substantially alleviating burdens in lithographic process. The effects of various geometrical parameters on the plasmonic properties were systematically investigated. Theoretical simulation on this structure demonstrates that the presence of vertical conductive bridge with smaller diameter sandwiched between two adjacent thin nanodiscs excites a bridged mode very similar to the charge transfer plasmon and exhibits a remarkable enhancement in the extinction efficiency and the sensitivity when the electric field of incident light is parallel to the conductive bridge. Furthermore, for the electric field perpendicular to the bridge, another interesting feature is observed that two magnetic resonance modes are excited symmetrically through open-gaps on both sides of the bridge together with strongly enhanced electric field intensity, which provides a very favorable environment as a surface enhanced Raman scattering substrate for fluid analysis. These results verify a great potential and versatility of our approach for use as a nanoplasmonic sensing platform. In addition, we demonstrated the feasibility of fabrication process of vertical conductive bridge and high tunability in controlling the bridge width.

Simplified calcium signaling cascade for synaptic plasticity.

We propose a model for synaptic plasticity based on a calcium signaling cascade. The model simplifies the full signaling pathways from a calcium influx to the phosphorylation (potentiation) and dephosphorylation (depression) of glutamate receptors that are gated by fictive C1 and C2 catalysts, respectively. This model is based on tangible chemical reactions, including fictive catalysts, for long-term plasticity rather than the conceptual theories commonplace in various models, such as preset thresholds of calcium concentration. Our simplified model successfully reproduced the experimental synaptic plasticity induced by different protocols such as (i) a synchronous pairing protocol and (ii) correlated presynaptic and postsynaptic action potentials (APs). Further, the ocular dominance plasticity (or the experimental verification of the celebrated Bienenstock-Cooper-Munro theory) was reproduced by two model synapses that compete by means of back-propagating APs (bAPs). The key to this competition is synapse-specific bAPs with reference to bAP-boosting on the physiological grounds.

Random nanohole arrays and its application to crystalline Si thin foils produced by proton induced exfoliation for solar cells.

We report high efficiency cell processing technologies for the ultra-thin Si solar cells based on crystalline Si thin foils (below a 50 µm thickness) produced by the proton implant exfoliation (PIE) technique. Shallow textures of submicrometer scale is essential for effective light trapping in crystalline Si thin foil based solar cells. In this study, we report the fabrication process of random Si nanohole arrays of ellipsoids by a facile way using low melting point metal nanoparticles of indium which were vacuum-deposited and dewetted spontaneously at room temperature. Combination of dry and wet etch processes with indium nanoparticles as etch masks enables the fabrication of random Si nanohole arrays of an ellipsoidal shape. The optimized etching processes led to effective light trapping nanostructures comparable to conventional micro-pyramids. We also developed the laser fired contact (LFC) process especially suitable for crystalline Si thin foil based PERC solar cells. The laser processing parameters were optimized to obtain a shallow LFC contact in conjunction with a low contact resistance. Lastly, we applied the random Si nanohole arrays and the LFC process to the crystalline Si thin foils (a 48 µm thickness) produced by the PIE technique and achieved the best efficiency of 17.1% while the planar PERC solar cell without the Si nanohole arrays exhibit 15.6%. Also, we demonstrate the ultra-thin wafer is bendable to have a 16 mm critical bending radius.

Artificial Neural Network for Response Inference of a Nonvolatile Resistance-Switch Array.

An artificial neural network was utilized in the behavior inference of a random crossbar array (10 × 9 or 28 × 27 in size) of nonvolatile binary resistance-switches (in a high resistance state (HRS) or low resistance state (LRS)) in response to a randomly applied voltage array. The employed artificial neural network was a multilayer perceptron (MLP) with leaky rectified linear units. This MLP was trained with 500,000 or 1,000,000 examples. For each example, an input vector consisted of the distribution of resistance states (HRS or LRS) over a crossbar array plus an applied voltage array. That is, for a M × N array where voltages are applied to its M rows, the input vector was M × (N + 1) long. The calculated (correct) current array for each random crossbar array was used as data labels for supervised learning. This attempt was successful such that the correlation coefficient between inferred and correct currents reached 0.9995 for the larger crossbar array. This result highlights MLP that leverages its versatility to capture the quantitative linkage between input and output across the highly nonlinear crossbar array.

A Ru-Pt alloy electrode to suppress leakage currents of dynamic random-access memory capacitors.

Rutile TiO2, a high temperature phase, has attracted interest as a capacitor dielectric in dynamic random-access memories (DRAMs). Despite its high dielectric constant of >80, large leakage currents caused by a low Schottky barrier height at the TiO2/electrode interface have hindered the use of rutile TiO2 as a commercial DRAM capacitor. Here, we propose a new Ru-Pt alloy electrode to increase the height of the Schottky barrier. The Ru-Pt mixed layer was grown by atomic layer deposition. The atomic ratio of Ru/Pt varied in the entire range from 100 at.% Ru to 100 at.% Pt. Rutile TiO2 films were inductively formed only on the Ru-Pt layer containing ≤43 at.% Pt, while anatase TiO2 films with a relatively low dielectric constant (∼40) were formed at Pt compositions > 63 at.%. The Ru-Pt (40-50 at.%) layer also attained an increase in work function of ∼0.3-0.4 eV, leading to an improvement in the leakage currents of the TiO2/Ru-Pt capacitor. These findings suggested that a Ru-Pt layer could serve as a promising electrode for next-generation DRAM capacitors.

Nonvolatile Memory Materials for Neuromorphic Intelligent Machines.

Recent progress in deep learning extends the capability of artificial intelligence to various practical tasks, making the deep neural network (DNN) an extremely versatile hypothesis. While such DNN is virtually built on contemporary data centers of the von Neumann architecture, physical (in part) DNN of non-von Neumann architecture, also known as neuromorphic computing, can remarkably improve learning and inference efficiency. Particularly, resistance-based nonvolatile random access memory (NVRAM) highlights its handy and efficient application to the multiply-accumulate (MAC) operation in an analog manner. Here, an overview is given of the available types of resistance-based NVRAMs and their technological maturity from the material- and device-points of view. Examples within the strategy are subsequently addressed in comparison with their benchmarks (virtual DNN in deep learning). A spiking neural network (SNN) is another type of neural network that is more biologically plausible than the DNN. The successful incorporation of resistance-based NVRAM in SNN-based neuromorphic computing offers an efficient solution to the MAC operation and spike timing-based learning in nature. This strategy is exemplified from a material perspective. Intelligent machines are categorized according to their architecture and learning type. Also, the functionality and usefulness of NVRAM-based neuromorphic computing are addressed.

Enhanced efficiency of crystalline Si solar cells based on kerfless-thin wafers with nanohole arrays.

Several techniques have been proposed for kerfless wafering of thin Si wafers, which is one of the most essential techniques for reducing Si material loss in conventional wafering methods to lower cell cost. Proton induced exfoliation is one of promising kerfless techniques due to the simplicity of the process of implantation and cleaving. However, for application to high efficiency solar cells, it is necessary to cope with some problems such as implantation damage removal and texturing of (111) oriented wafers. This study analyzes the end-of-range defects at both kerfless and donor wafers and ion cutting sites. Thermal treatment and isotropic etching processes allow nearly complete removal of implantation damages in the cleaved-thin wafers. Combining laser interference lithography and a reactive ion etch process, a facile nanoscale texturing process for the kerfless thin wafers of a (111) crystal orientation has been developed. We demonstrate that the introduction of nanohole array textures with an optimal design and complete damage removal lead to an improved efficiency of 15.2% based on the kerfless wafer of a 48 µm thickness using the standard architecture of the Al back surface field.

Scalable excitatory synaptic circuit design using floating gate based leaky integrators.

We propose a scalable synaptic circuit realizing spike timing dependent plasticity (STDP)-compatible with randomly spiking neurons. The feasible working of the circuit was examined by circuit simulation using the BSIM 4.6.0 model. A distinguishable feature of the circuit is the use of floating-gate integrators that provide the compact implementation of biologically plausible relaxation time scale. This relaxation occurs on the basis of charge tunneling that mainly relies upon area-independent tunnel barrier properties (e.g. barrier width and height) rather than capacitance. The circuit simulations feature (i) weight-dependent STDP that spontaneously limits the synaptic weight growth, (ii) competitive synaptic adaptation within both unsupervised and supervised frameworks with randomly spiking neurons. The estimated power consumption is merely 34 pW, perhaps meeting one of the most crucial principles (power-efficiency) of neuromorphic engineering. Finally, a means of fine-tuning the STDP behavior is provided.

Fabrication of parabolic Si nanostructures by nanosphere lithography and its application for solar cells.

We demonstrated fabrication of a parabola shaped Si nanostructures of various periods by combined approach of nanosphere lithography and a single step CF4/O2 reactive ion etch (RIE) process. Silica nanosphere monolayers in a hexagonal array were well deposited by a solvent controlled spin coating technique based on binary organic solvents. We showed numerically that a parabolic Si nanostructure of an optimal period among various-shaped nanostructures overcoated with a dielectric layer of a 70 nm thickness provide the most effective antireflection. As the simulation results as a design guide, we fabricated the parabolic Si nanostructures of a 520 nm period and a 300 nm height exhibiting the lowest weighted reflectance of 2.75%. With incorporation of such parabolic Si nanostructures, a damage removal process for 20 sec and SiNx antireflection coating of a 70 nm thickness, the efficiency of solar cells increased to 17.2% while that of the planar cells without the nanostructures exhibited 16.2%. The efficiency enhancement of the cell with the Si nanostructures was attributed to the improved photocurrents arising from the broad spectral antireflection which was confirmed by the external quantum efficiency (EQE) measurements.

Chameleonic electrochemical metallization cells: dual-layer solid electrolyte-inducing various switching behaviours.

We present 'unusual' resistive switching behaviours in electrochemical metallization (ECM) cells utilizing a dual-layer (SiOx/GeSex: SiOx on GeSex) solid electrolyte (SE). The observed switching behaviour markedly varies with the thickness of the upper SiOx layer and compliance current: (i) monostable switching, (ii) counter-eightwise bipolar switching, and (iii) combination of monostable and eightwise bipolar switching behaviours. Focusing on cases (i) and (iii), electrical and chemical analyses on these chameleonic cells were performed in an attempt to gain clues to the understanding of the observed complexity. The chemical analysis indicated the upper SiOx layer as a chemical potential well for Cu ions-Cu ions were largely confined in the well. This non-uniform distribution of Cu across the SE perhaps hints at the mechanism for the complex behaviour; it may be a 'zero-sum game' between SiOx and GeSex layers, in which the two layers fight over the limited number of Cu atoms/ions.

Leaky Integrate-and-Fire Neuron Circuit Based on Floating-Gate Integrator.

The artificial spiking neural network (SNN) is promising and has been brought to the notice of the theoretical neuroscience and neuromorphic engineering research communities. In this light, we propose a new type of artificial spiking neuron based on leaky integrate-and-fire (LIF) behavior. A distinctive feature of the proposed FG-LIF neuron is the use of a floating-gate (FG) integrator rather than a capacitor-based one. The relaxation time of the charge on the FG relies mainly on the tunnel barrier profile, e.g., barrier height and thickness (rather than the area). This opens up the possibility of large-scale integration of neurons. The circuit simulation results offered biologically plausible spiking activity (<100 Hz) with a capacitor of merely 6 fF, which is hosted in an FG metal-oxide-semiconductor field-effect transistor. The FG-LIF neuron also has the advantage of low operation power (<30 pW/spike). Finally, the proposed circuit was subject to possible types of noise, e.g., thermal noise and burst noise. The simulation results indicated remarkable distributional features of interspike intervals that are fitted to Gamma distribution functions, similar to biological neurons in the neocortex.

Polarity-tunable spin transport in all-oxide multiferroic tunnel junctions.

A multiferroic tunnel junction (MFTJ) promisingly offers multinary memory states in response to electric- and magnetic-fields, referring to tunneling electroresistance (TER) and tunneling magnetoresistance (TMR), respectively. In spite of recent progress, a substantial number of questions concerning the understanding of these two intertwined phenomena still remain open, e.g. the role of microstructural/chemical asymmetry at the interfaces of the junction and the effect of an electrode material on the MFTJ properties. In this regard, we look into the multiferroic effect of all-complex-oxide MFTJ (La0.7Sr0.3MnO3/Pb(Zr0.3Ti0.7)O3/La0.7Sr0.3MnO3). The results reveal apparent TER-TMR interplay-captured by the reversible electric-field control of the TMR effect. Finally, microscopy analysis on the MFTJ revealed that the observed TER-TMR interplay is perhaps mediated by microstructural and chemical asymmetry in our nominally symmetric MFTJ.

Wafer-scale growth of MoS2 thin films by atomic layer deposition.

The wafer-scale synthesis of MoS2 layers with precise thickness controllability and excellent uniformity is essential for their application in the nanoelectronics industry. Here, we demonstrate the atomic layer deposition (ALD) of MoS2 films with Mo(CO)6 and H2S as the Mo and S precursors, respectively. A self-limiting growth behavior is observed in the narrow ALD window of 155-175 °C. Long H2S feeding times are necessary to reduce the impurity contents in the films. The as-grown MoS2 films are amorphous due to the low growth temperature. Post-annealing at high temperatures under a H2S atmosphere efficiently improves the film properties including the crystallinity and chemical composition. An extremely uniform film growth is achieved even on a 4 inch SiO2/Si wafer. These results demonstrate that the current ALD process is well suited for the synthesis of MoS2 layers for application in industry.

Inherently-Forced Tensile Strain in Nanodiamond-Derived Onion-like Carbon: Consequences in Defect-Induced Electrochemical Activation.

We analyzed the nanodiamond-derived onion-like carbon (OLC) as function of synthesis temperature (1000~1400 °C), by high-resolution electron microscopy, electron energy loss spectroscopy, visible-Raman spectroscopy, ultraviolet photoemission spectroscopy, impedance spectroscopy, cyclic voltammetry and differential pulse voltammetry. The temperature dependences of the obtained properties (averaged particle size, tensile strain, defect density, density of states, electron transfer kinetics, and electrochemical oxidation current) unanimously coincided: they initially increased and saturated at 1200 °C. It was attributed to the inherent tensile strains arising from (1) the volume expansion associated with the layer-wise diamond-to-graphite transformation of the core, which caused forced dilation of the outer shells during their thermal synthesis; (2) the extreme curvature of the shells. The former origin was dominant over the latter at the outermost shell, of which the relevant evolution in defect density, DOS and electron transfer kinetics determined the electrochemical performances. In detection of dopamine (DA), uric acid (UA) and ascorbic acid (AA) using the OLC as electrode, their oxidation peak currents were enhanced by factors of 15~60 with annealing temperature. Their limit of detection and the linear range of detection, in the post-treatment-free condition, were as excellent as those of the nano-carbon electrodes post-treated by Pt-decoration, N-doping, plasma, or polymer.

Relaxation oscillator-realized artificial electronic neurons, their responses, and noise.

A proof-of-concept relaxation oscillator-based leaky integrate-and-fire (ROLIF) neuron circuit is realized by using an amorphous chalcogenide-based threshold switch and non-ideal operational amplifier (op-amp). The proposed ROLIF neuron offers biologically plausible features such as analog-type encoding, signal amplification, unidirectional synaptic transmission, and Poisson noise. The synaptic transmission between pre- and postsynaptic neurons is achieved through a passive synapse (simple resistor). The synaptic resistor coupled to the non-ideal op-amp realizes excitatory postsynaptic potential (EPSP) evolution that evokes postsynaptic neuron spiking. In an attempt to generalize our proposed model, we theoretically examine ROLIF neuron circuits adopting different non-ideal op-amps having different gains and slew rates. The simulation results indicate the importance of gain in postsynaptic neuron spiking, irrespective of the slew rate (as long as the rate exceeds a particular value), providing the basis for the ROLIF neuron circuit design. Eventually, the behavior of a postsynaptic neuron in connection to multiple presynaptic neurons via synapses is highlighted in terms of EPSP evolution amid simultaneously incident asynchronous presynaptic spikes, which in fact reveals an important role of the random noise in spatial integration.

Frustration of Negative Capacitance in Al2O3/BaTiO3 Bilayer Structure.

Enhancement of capacitance by negative capacitance (NC) effect in a dielectric/ferroelectric (DE/FE) stacked film is gaining a greater interest. While the previous theory on NC effect was based on the Landau-Ginzburg-Devonshire theory, this work adopted a modified formalism to incorporate the depolarization effect to describe the energy of the general DE/FE system. The model predicted that the SrTiO3/BaTiO3 system will show a capacitance boost effect. It was also predicted that the 5 nm-thick Al2O3/150 nm-thick BaTiO3 system shows the capacitance boost effect with no FE-like hysteresis behavior, which was inconsistent with the experimental results; the amorphous-Al2O3/epitaxial-BaTiO3 system showed a typical FE-like hysteresis loop in the polarization - voltage test. This was due to the involvement of the trapped charges at the DE/FE interface, originating from the very high field across the thin Al2O3 layer when the BaTiO3 layer played a role as the NC layer. Therefore, the NC effect in the Al2O3/BaTiO3 system was frustrated by the involvement of reversible interface charge; the highly stored charge by the NC effect of the BaTiO3 during the charging period could not be retrieved during the discharging process because integral part of the polarization charge was retained within the system as a remanent polarization.