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.

Control of the initial growth in atomic layer deposition of Pt films by surface pretreatment.

The controllability of the nucleation behavior of Pt in atomic layer deposition (ALD) by surface pretreatments with H2O, H2S, and NH3 was investigated. The H2O pretreatment on SiO2 and TiO2 surfaces had little effect on the nucleation of Pt. The H2S pretreatment on the SiO2 and TiO2 surfaces significantly delayed the nucleation of Pt on them, while the NH3 pretreatment on the TiO2 surface led to fluent nucleation of Pt. In particular, a continuous Pt film was successfully formed even at an ultrathin thickness of approximately 2.2 nm by NH3 pretreatment. This work suggests that the pretreatment with H2S and NH3 is an efficient way to control the nucleation of Pt in ALD without the support of any reactive species, such as plasma or O3. Such a strategy enables the easy control of the size and distribution density of Pt nanoparticles for a wide range of applications.

Silicon nanodisk array design for effective light trapping in ultrathin c-Si.

The use of ultrathin c-Si (crystalline silicon) wafers thinner than 20 µm for solar cells is a very promising approach to realize dramatic reduction in cell cost. However, the ultrathin c-Si requires highly effective light trapping to compensate optical absorption reduction. Conventional texturing in micron scale is hardly applicable to the ultrathin c-Si wafers; thus, nano scale texturing is demanded. In general, nanotexturing is inevitably accompanied by surface area enlargements, which must be minimized in order to suppress surface recombination of minority carriers. In this study, we demonstrate using optical simulations that periodic c-Si nanodisk arrays of short heights less than 200 nm and optimal periods are very useful in terms of light trapping in the ultrathin c-Si wafers while low surface area enlargements are maintained. Double side texturing with the nanodisk arrays leads to over 90% of the Lambertian absorption limit while the surface area enlargement is kept below 1.5.

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.

Optical design of transparent metal grids for plasmonic absorption enhancement in ultrathin organic solar cells.

Transparent metal grid combining with plasmonic absorption enhancement is a promising replacement to indium tin oxide thin films. We numerically demonstrate metal grids in one or two dimension lead to plasmonic absorption enhancements in ultrathin organic solar cells. In this paper, we study optical design of metal grids for plasmonic light trapping and identify different plasmonic modes of the surface plasmon polaritons excited at the interfaces of glass/metal grids, metal grids/active layers, and the localized surface plasmon resonance of the metal grids using numerical calculations. One dimension metal grids with the optimal design of a width and a period lead to the absorption enhancement in the ultrathin active layers of 20 nm thickness by a factor of 2.6 under transverse electric polarized light compared to the case without the metal grids. Similarly, two dimensional metal grids provide the absorption enhancement by a factor of 1.8 under randomly polarized light.

Short-term memory of TiO2-based electrochemical capacitors: empirical analysis with adoption of a sliding threshold.

Chemical synapses are important components of the large-scaled neural network in the hippocampus of the mammalian brain, and a change in their weight is thought to be in charge of learning and memory. Thus, the realization of artificial chemical synapses is of crucial importance in achieving artificial neural networks emulating the brain's functionalities to some extent. This kind of research is often referred to as neuromorphic engineering. In this study, we report short-term memory behaviours of electrochemical capacitors (ECs) utilizing TiO2 mixed ionic-electronic conductor and various reactive electrode materials e.g. Ti, Ni, and Cr. By experiments, it turned out that the potentiation behaviours did not represent unlimited growth of synaptic weight. Instead, the behaviours exhibited limited synaptic weight growth that can be understood by means of an empirical equation similar to the Bienenstock-Cooper-Munro rule, employing a sliding threshold. The observed potentiation behaviours were analysed using the empirical equation and the differences between the different ECs were parameterized.

Emerging memories: resistive switching mechanisms and current status.

The resistance switching behaviour of several materials has recently attracted considerable attention for its application in non-volatile memory (NVM) devices, popularly described as resistive random access memories (RRAMs). RRAM is a type of NVM that uses a material(s) that changes the resistance when a voltage is applied. Resistive switching phenomena have been observed in many oxides: (i) binary transition metal oxides (TMOs), e.g. TiO(2), Cr(2)O(3), FeO(x) and NiO; (ii) perovskite-type complex TMOs that are variously functional, paraelectric, ferroelectric, multiferroic and magnetic, e.g. (Ba,Sr)TiO(3), Pb(Zr(x) Ti(1-x))O(3), BiFeO(3) and Pr(x)Ca(1-x)MnO(3); (iii) large band gap high-k dielectrics, e.g. Al(2)O(3) and Gd(2)O(3); (iv) graphene oxides. In the non-oxide category, higher chalcogenides are front runners, e.g. In(2)Se(3) and In(2)Te(3). Hence, the number of materials showing this technologically interesting behaviour for information storage is enormous. Resistive switching in these materials can form the basis for the next generation of NVM, i.e. RRAM, when current semiconductor memory technology reaches its limit in terms of density. RRAMs may be the high-density and low-cost NVMs of the future. A review on this topic is of importance to focus concentration on the most promising materials to accelerate application into the semiconductor industry. This review is a small effort to realize the ambitious goal of RRAMs. Its basic focus is on resistive switching in various materials with particular emphasis on binary TMOs. It also addresses the current understanding of resistive switching behaviour. Moreover, a brief comparison between RRAMs and memristors is included. The review ends with the current status of RRAMs in terms of stability, scalability and switching speed, which are three important aspects of integration onto semiconductors.

Plasmonic nanograting design for inverted polymer solar cells.

Plasmonic nanostructures for effective light trapping in a variety of photovoltaics have been actively studied. Metallic nanograting structures are one of promising architectures. In this study, we investigated numerically absorption enhancement mechanisms in inverted polymer photovoltaics with one dimensional Ag nanograting in backcontact. An optical spacer layer of TiO2, which also may act as an electron transport layer, was introduced between nanograting pillars. Using a finite-difference-time domain method and performing a modal analysis, we explored correlations between absorption enhancements and dimensional parameters of nanograting such as period as well as height and width. The optimal design of nanograting for effective light trapping especially near optical band gap of an active layer was discussed, and 23% of absorption enhancement in a random polarization was demonstrated numerically with the optimally designed nanograting. In addition, the beneficial role of the optical spacer in plasmonic light trapping was also discussed.

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.

Nanofilamentary resistive switching in binary oxide system; a review on the present status and outlook.

This review article summarized the recent understanding of resistance switching (RS) behavior in several binary oxide thin film systems. Among the various RS materials and mechanisms, TiO(2) and NiO thin films in unipolar thermo-chemical switching mode are primarily dealt with. To facilitate the discussions, the RS was divided into three parts; electroforming, set and reset steps. After short discussions on the electrochemistry of 'electrolytic' oxide materials, the general and peculiar aspects of these RS systems and mechanism are elaborated. Although the RS behaviors and characteristics of these materials are primarily dependent on the repeated formation and rupture of the conducting filaments (CFs) at the nanoscale at a localized position, this mechanism appears to offer a basis for the understanding of other RS mechanisms which were originally considered to be irrelevant to the localized events. The electroforming and set switching phenomena were understood as the process of CF formation and rejuvenation, respectively, which are mainly driven by the thermally assisted electromigration and percolation (or even local phase transition) of defects, while the reset process was understood as the process of CF rupture where the thermal energy plays a more crucial role. This review also contains several remarks on the outlook of these resistance change devices as a semiconductor memory.

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.

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.

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.

Abnormal bipolar-like resistance change behavior induced by symmetric electroforming in Pt/TiO2/Pt resistive switching cells.

Abnormal bipolar-like resistive changes are reported in TiO(2) thin films sandwiched between Pt top and bottom electrodes. The abnormal behavior is shown relying on the applied voltage range. That is, normal bipolar switching is also shown in the same sample with the optimized voltage range. In the abnormal mode, both set- and reset-like changes in resistance take place under the same polarity of the applied voltage. This abnormal behavior is considered to be due to symmetric electroforming which is assumed to activate electrochemical reactions involving oxygen vacancies at both Pt/TiO(2) interfaces. We analyze the abnormal behavior in terms of the interfacial resistive switching taking place on both interfaces nearly simultaneously.

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.

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.

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.