Memristors with Tunable Volatility for Reconfigurable Neuromorphic Computing.

Neuromorphic computing promises an energy-efficient alternative to traditional digital processors in handling data-heavy tasks, primarily driven by the development of both volatile (neuronal) and nonvolatile (synaptic) resistive switches or memristors. However, despite their energy efficiency, memristor-based technologies presently lack functional tunability, thus limiting their competitiveness with arbitrarily programmable (general purpose) digital computers. This work introduces a two-terminal bilayer memristor, which can be tuned among neuronal, synaptic, and hybrid behaviors. The varying behaviors are accessed via facile control over the filament formed within the memristor, enabled by the interplay between the two active ionic species (oxygen vacancies and metal cations). This solution is unlike single-species ion migration employed in most other memristors, which makes their behavior difficult to control. By reconfiguring a single crossbar array of hybrid memristors, two different applications that usually require distinct types of devices are demonstrated - reprogrammable heterogeneous reservoir computing and arbitrary non-Euclidean graph networks. Thus, this work outlines a potential path toward functionally reconfigurable postdigital computers.

True random number generation using the spin crossover in LaCoO3.

While digital computers rely on software-generated pseudo-random number generators, hardware-based true random number generators (TRNGs), which employ the natural physics of the underlying hardware, provide true stochasticity, and power and area efficiency. Research into TRNGs has extensively relied on the unpredictability in phase transitions, but such phase transitions are difficult to control given their often abrupt and narrow parameter ranges (e.g., occurring in a small temperature window). Here we demonstrate a TRNG based on self-oscillations in LaCoO3 that is electrically biased within its spin crossover regime. The LaCoO3 TRNG passes all standard tests of true stochasticity and uses only half the number of components compared to prior TRNGs. Assisted by phase field modeling, we show how spin crossovers are fundamentally better in producing true stochasticity compared to traditional phase transitions. As a validation, by probabilistically solving the NP-hard max-cut problem in a memristor crossbar array using our TRNG as a source of the required stochasticity, we demonstrate solution quality exceeding that using software-generated randomness.

Implementation of Bayesian networks and Bayesian inference using a Cu0.1Te0.9/HfO2/Pt threshold switching memristor.

Bayesian networks and Bayesian inference, which forecast uncertain causal relationships within a stochastic framework, are used in various artificial intelligence applications. However, implementing hardware circuits for the Bayesian inference has shortcomings regarding device performance and circuit complexity. This work proposed a Bayesian network and inference circuit using a Cu0.1Te0.9/HfO2/Pt volatile memristor, a probabilistic bit neuron that can control the probability of being 'true' or 'false.' Nodal probabilities within the network are feasibly sampled with low errors, even with the device's cycle-to-cycle variations. Furthermore, Bayesian inference of all conditional probabilities within the network is implemented with low power (<186 nW) and energy consumption (441.4 fJ), and a normalized mean squared error of ∼7.5 × 10-4 through division feedback logic with a variational learning rate to suppress the inherent variation of the memristor. The suggested memristor-based Bayesian network shows the potential to replace the conventional complementary metal oxide semiconductor-based Bayesian estimation method with power efficiency using a stochastic computing method.

Tunable stochastic memristors for energy-efficient encryption and computing.

Information security and computing, two critical technological challenges for post-digital computation, pose opposing requirements - security (encryption) requires a source of unpredictability, while computing generally requires predictability. Each of these contrasting requirements presently necessitates distinct conventional Si-based hardware units with power-hungry overheads. This work demonstrates Cu0.3Te0.7/HfO2 ('CuTeHO') ion-migration-driven memristors that satisfy the contrasting requirements. Under specific operating biases, CuTeHO memristors generate truly random and physically unclonable functions, while under other biases, they perform universal Boolean logic. Using these computing primitives, this work experimentally demonstrates a single system that performs cryptographic key generation, universal Boolean logic operations, and encryption/decryption. Circuit-based calculations reveal the energy and latency advantages of the CuTeHO memristors in these operations. This work illustrates the functional flexibility of memristors in implementing operations with varying component-level requirements.

2Memristor-1Capacitor Integrated Temporal Kernel for High-Dimensional Data Mapping.

Compact but precise feature-extracting ability is core to processing complex computational tasks in neuromorphic hardware. Physical reservoir computing (RC) offers a robust framework to map temporal data into a high-dimensional space using the time dynamics of a material system, such as a volatile memristor. However, conventional physical RC systems have limited dynamics for the given material properties, restricting the methods to increase their dimensionality. This study proposes an integrated temporal kernel composed of a 2-memristor and 1-capacitor (2M1C) using a W/HfO2/TiN memristor and TiN/ZrO2/Al2O3/ZrO2/TiN capacitor to achieve higher dimensionality and tunable dynamics. The kernel elements are carefully designed and fabricated into an integrated array, of which performances are evaluated under diverse conditions. By optimizing the time dynamics of the 2M1C kernel, each memristor simultaneously extracts complementary information from input signals. The MNIST benchmark digit classification task achieves a high accuracy of 94.3% with a (196×10) single-layer network. Analog input mapping ability is tested with a Mackey-Glass time series prediction, and the system records a normalized root mean square error of 0.04 with a 20×1 readout network, the smallest readout network ever used for Mackey-Glass prediction in RC. These performances demonstrate its high potential for efficient temporal data analysis.

Heterogeneous reservoir computing in second-order Ta2O5/HfO2 memristors.

Multiple switching modes in a Ta2O5/HfO2 memristor are studied experimentally and numerically through a reservoir computing (RC) simulation to reveal the importance of nonlinearity and heterogeneity in the RC framework. Unlike most studies, where homogeneous reservoirs are used, heterogeneity is introduced by combining different behaviors of the memristor units. The chosen memristor for the reservoir units is based on a Ta2O5/HfO2 bilayer, in which the conductances of the Ta2O5 and HfO2 layers are controlled by the oxygen vacancies and deep/shallow traps, respectively, providing both volatile and non-volatile resistive switching modes. These several control parameters make the second-order Ta2O5/HfO2 memristor system present different behaviors in agreement with its history-dependent conductance and allow the fine-tuning of the behavior of each reservoir unit. The heterogeneity in the reservoir units improves the pattern recognition performance in the heterogeneous memristor RC system with a similar physical structure.

Spatiotemporal Data Processing with Memristor Crossbar-Array-Based Graph Reservoir.

Memristor-based physical reservoir computing (RC) is a robust framework for processing complex spatiotemporal data parallelly. However, conventional memristor-based reservoirs cannot capture the spatial relationship between the time-varying inputs due to the specific mapping scheme assigning one input signal to one memristor conductance. Here, a physical "graph reservoir" is introduced using a metal cell at the diagonal-crossbar array (mCBA) with dynamic self-rectifying memristors. Input and inverted input signals are applied to the word and bit lines of the mCBA, respectively, storing the correlation information between input signals in the memristors. In this way, the mCBA graph reservoirs can map the spatiotemporal correlation of the input data in a high-dimensional feature space. The high-dimensional mapping characteristics of the graph reservoir achieve notable results, including a normalized root-mean-square error of 0.09 in Mackey-Glass time series prediction, a 97.21% accuracy in MNIST recognition, and an 80.0% diagnostic accuracy in human connectome classification.

Graph Analysis with Multifunctional Self-Rectifying Memristive Crossbar Array.

Many big data have interconnected and dynamic graph structures growing over time. Analyzing these graphical data requires the hidden relationship between the nodes in the graphs to be identified, which has conventionally been achieved by finding the effective similarity. However, graphs are generally non-Euclidean, which does not allow finding it. In this study, the non-Euclidean graphs are mapped to a specific crossbar array (CBA) composed of self-rectifying memristors and metal cells at the diagonal positions. The sneak current, an intrinsic physical property in the CBA, allows for the identification of the similarity function. The sneak-current-based similarity function indicates the distance between the nodes, which can be used to predict the probability that unconnected nodes will be connected in the future, connectivity between communities, and neural connections in a brain. When all bit lines of the CBA are connected to the ground, the sneak current is suppressed, and the CBA can be used to search for adjacent nodes. This work demonstrates the physical calculation methods applied to various graphical problems using the CBA composed of the self-rectifying memristor based on the HfO2 switching layer. Moreover, such applications suffer less from the memristors' inherent issues related to their stochastic nature.

Probabilistic computing using Cu0.1Te0.9/HfO2/Pt diffusive memristors.

A computing scheme that can solve complex tasks is necessary as the big data field proliferates. Probabilistic computing (p-computing) paves the way to efficiently handle problems based on stochastic units called probabilistic bits (p-bits). This study proposes p-computing based on the threshold switching (TS) behavior of a Cu0.1Te0.9/HfO2/Pt (CTHP) diffusive memristor. The theoretical background of the p-computing resembling the Hopfield network structure is introduced to explain the p-computing system. P-bits are realized by the stochastic TS behavior of CTHP diffusive memristors, and they are connected to form the p-computing network. The memristor-based p-bit is likely to be '0' and '1', of which probability is controlled by an input voltage. The memristor-based p-computing enables all 16 Boolean logic operations in both forward and inverted operations, showing the possibility of expanding its uses for complex operations, such as full adder and factorization.

Time-varying data processing with nonvolatile memristor-based temporal kernel.

Recent advances in physical reservoir computing, which is a type of temporal kernel, have made it possible to perform complicated timing-related tasks using a linear classifier. However, the fixed reservoir dynamics in previous studies have limited application fields. In this study, temporal kernel computing was implemented with a physical kernel that consisted of a W/HfO2/TiN memristor, a capacitor, and a resistor, in which the kernel dynamics could be arbitrarily controlled by changing the circuit parameters. After the capability of the temporal kernel to identify the static MNIST data was proven, the system was adopted to recognize the sequential data, ultrasound (malignancy of lesions) and electrocardiogram (arrhythmia), that had a significantly different time constant (10-7 vs. 1 s). The suggested system feasibly performed the tasks by simply varying the capacitance and resistance. These functionalities demonstrate the high adaptability of the present temporal kernel compared to the previous ones.

Electroforming-Free Bipolar Resistive Switching in GeSe Thin Films with a Ti-Containing Electrode.

Chalcogenide materials have been regarded as strong candidates for both resistor and selector elements in passive crossbar arrays owing to their dual capabilities of undergoing threshold and resistance switching. This work describes the bipolar resistive switching (BRS) of amorphous GeSe thin films, which used to show Ovonic threshold switching (OTS) behavior. The behavior of this new functionality of the material follows filament-based resistance switching when Ti and TiN are adopted as the top and bottom electrodes, respectively. The detailed analysis revealed that the high chemical affinity of Ti to Se produces a Se-deficient GexSe1-x matrix and the interfacial Ti-Se layer. Electroforming-free BRS behavior with reliable retention and cycling endurance was achieved. The performance improvement was attributed to the Ti-Se interfacial layer, which stabilizes the composition of GeSe during the electrical switching cycles by preventing further massive Se migration to the top electrode. The conduction mechanism analysis denotes that the resistance switching originates from the formation and rupture of the high-conductance semiconducting Ge-rich GexSe1-x filament. The high-resistance state follows the modified Poole-Frenkel conduction.

Atomic layer deposition of GeSe films using HGeCl3 and [(CH3)3Si]2Se with the discrete feeding method for the ovonic threshold switch.

The ovonic threshold switch (OTS) based on the voltage snapback of amorphous chalcogenides possesses several desirable characteristics: bidirectional switching, a controllable threshold voltage (V th) and processability for three-dimensional stackable devices. Among the materials that can be used as OTS, GeSe has a strong glass-forming ability (∼350 °C crystallization temperature), with a simple binary composition. Described herein is a new method of depositing GeSe films through atomic layer deposition (ALD), using HGeCl3 and [(CH3)3Si]2Se as Ge and Se precursors, respectively. The stoichiometric GeSe thin films were formed through a ligand exchange reaction between the two precursor molecules, without the adoption of an additional reaction gas, at low substrate temperatures ranging from 70 °C-150 °C. The pseudo-saturation behavior required a long time of Ge precursor injection to achieve the saturation growth rate. This was due to the adverse influence of the physisorbed precursor and byproduct molecules on the efficient chemical adsorption reaction between the precursors and reaction sites. To overcome the slow saturation and excessive use of the Ge precursor, the discrete feeding method (DFM), where HGeCl3 is supplied multiple times consecutively with subdivided pulse times, was adopted. DFM led to the saturation of the GeSe growth rate at a much shorter total injection time of the Ge precursor, and improved the film density and oxidation resistance properties. The GeSe film grown via DFM exhibited a short OTS time of ∼40 ns, a ∼107 ON/OFF current ratio, and ∼104 selectivity. The OTS behavior was consistent with the modified Poole-Frenkel mechanism in the OFF state. In contrast, the similar GeSe film grown through the conventional ALD showed a low density and high vulnerability to oxidation, which prevented the OTS performance. The ALD method of GeSe films introduced here will contribute to the fabrication of a three-dimensionally integrated memory as a selector device for preventing sneak current.