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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.
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 implemen
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 t
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
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 u
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 representatio