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Register a new userSynaptic Learning and Memory Functions Achieved in Self-rectifying BFO Memristor under Extreme Environmental Temperature
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Memristors have been intensively studied in recent years as promising building blocks for next-generation non-volatile memory, artificial neural networks and brain-inspired computing systems. Even though the environment adaptability of memristor has been required in many application fields, it has been rarely reported due to the underlying mechanism could become invalid especially at an elevated temperature. Here, we focus on achieving synaptic learning and memory functions in BiFeO3 memristor in a wide range of temperature. We have proved the ferroelectricity of BFO films at a record-high temperature of 500 {deg}C by piezoresponse force microscopy (PFM) measurement. Due to the robust ferroelectricity of BFO thin film, an analog-like resistance switching behavior has been clearly found in a wide range of temperature, which is attributed to the reversal of ferroelectric polarization. Various synaptic functions including long-term potentiation (LTP), depression (LTD), consecutive potentiation/depression (P/D) and spike-timing dependent plasticity (STDP) have been realized from -170 to 300 {deg}C, illustrating their potential for electronic applications even under extreme environmental temperature.
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We present new computational building blocks based on memristive devices. These blocks, can be used to implement either supervised or unsupervised learning modules. This is achieved using a crosspoint architecture which is an efficient array implementation for nanoscale two-terminal memristive devices. Based on these blocks and an experimentally verified SPICE macromodel for the memristor, we demonstrate that firstly, the Spike-Timing-Dependent Plasticity (STDP) can be implemented by a single memristor device and secondly, a memristor-based competitive Hebbian learning through STDP using a $1times 1000$ synaptic network. This is achieved by adjusting the memristors conductance values (weights) as a function of the timing difference between presynaptic and postsynaptic spikes. These implementations have a number of shortcomings due to the memristors characteristics such as memory decay, highly nonlinear switching behaviour as a function of applied voltage/current, and functional uniformity. These shortcomings can be addressed by utilising a mixed gates that can be used in conjunction with the analogue behaviour for biomimetic computation. The digital implementations in this paper use in-situ computational capability of the memristor.
Synaptic Sampling Machine (SSM) is a type of neural network model that considers biological unreliability of the synapses. We propose the circuit design of the SSM neural network which is realized through the memristive-CMOS crossbar structure with the synaptic sampling cell (SSC) being used as a basic stochastic unit. The increase in the edge computing devices in the Internet of things era, drives the need for hardware acceleration for data processing and computing. The computational considerations of the processing speed and possibility for the real-time realization pushes the synaptic sampling algorithm that demonstrated promising results on software for hardware implementation.
We present a combined theoretical and experimental study of the high-pressure behavior of thallium. X-ray diffraction experiments have been carried out at room temperature up to 125 GPa using diamond-anvil cells, nearly doubling the pressure range of previous experiments. We have confirmed the hcp-fcc transition at 3.5 GPa and determined that the fcc structure remains stable up to the highest pressure attained in the experiments. In addition, HP-HT experiments have been performed up to 8 GPa and 700 K by using a combination of x-ray diffraction and a resistively heated diamond-anvil cell. Information on the phase boundaries is obtained, as well as crystallographic information on the HT bcc phase. The equation of state for different phases is reported. Ab initio calculations have also been carried out considering several potential high-pressure structures. They are consistent with the experimental results and predict that, among the structures considered in the calculations, the fcc structure of thallium is stable up to 4.3 TPa. Calculations also predict the post-fcc phase to have a close-packed orthorhombic structure above 4.3 TPa.
Remarkably high values of polarization as well as a significant magnetic susceptibility have been observed in multiferroic Bismuth Ferrite (BFO) in the form of nanorods protruding out. These were developed on porous Anodised Alumina (AAO) templates using wet chemical technique. Diameters of nanorods are in the range of 20-100 nm. The high values of polarization and magnetic susceptibility are attributed to the BFO nanorod structures giving rise to the directionality. There is no leakage current in P-E loop examined at various frequencies. Magnetocapacitance measurements reflect a significant enhancement in magnetoelectric coupling also.
While machine learning (ML) in experimental research has demonstrated impressive predictive capabilities, inductive reasoning and knowledge extraction remain elusive tasks, in part because of the difficulty extracting fungible knowledge representations from experimental data. In this manuscript, we use ML to infer the underlying dynamical differential equation (DE) from experimental data of degrading organic-inorganic methylammonium lead iodide (MAPI) perovskite thin films under environmental stressors (elevated temperature, humidity, and light). We apply a sparse regression algorithm that automatically identifies the differential equation describing the dynamics from time-series data. We find that the underlying DE governing MAPI degradation across a broad temperature range of 35 to 85{deg}C is described minimally with three terms (specifically, a second-order polynomial), and not a simple single-order reaction (i.e. 0th, 1st, or 2nd-order reaction). We demonstrate how computer-derived results can aid the researcher to develop profound mechanistic insights. This DE corresponds to the Verhulst logistic function, which describes reaction kinetics analogous in functional form to autocatalytic or self-propagating reactions, suggesting future strategies to suppress MAPI degradation. We examine the robustness of our conclusions to experimental luck-of-the-draw variance and Gaussian noise using a combination of experiment and simulation, and describe the experimental limits within which this methodology can be applied. Our study demonstrates the application of scientific ML in experimental chemical and materials systems, highlighting the promise and challenges associated with ML-aided scientific discovery.
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