A compact neuromorphic nanodevice with inherent learning and memory properties emulating those of biological synapses is the key to developing artificial neural networks rivaling their biological counterparts. Experimental results showed that memorization with a wide time scale from volatile to permanent can be achieved in a WO3-x-based nanoionics device and can be precisely and cumulatively controlled by adjusting the device's resistance state and input pulse parameters such as the amplitude, interval, and number. This control is analogous to biological synaptic plasticity including short-term plasticity, long-term potentiation, transition from short-term memory to long-term memory, forgetting processes for short- and long-term memory, learning speed, and learning history. A compact WO3-x-based nanoionics device with a simple stacked layer structure should thus be a promising candidate for use as an inorganic synapse in artificial neural networks due to its striking resemblance to the biological synapse.

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A spin cluster glass behavior and a complicated exchange bias effect are observed in high quality BiFeO(3) nanocrystals grown by a hydrothermal method. The dynamic properties of the spin clusters investigated by measuring the frequency dependences of ac susceptibility show that the relaxation process can be described using a power law with the glass transition temperature T(g) = 57 K, relaxation time constant τ(0) = 4.4 × 10(-10) s, and critical exponent zv = 10.3 ± 1.9, consistent with a three-dimensional Ising spin glass. The exchange bias field (H(EB)) varies non-monotonically with temperature and achieves a minimum at T(g). The abnormal shift of hysteresis loops above T(g) may be interpreted in terms of a Malozemoff's random-field model with a framework of antiferromagnetic core/spin-cluster shell structure and a two-dimensional diluted antiferromagnet in a field (2D-DAFF) model, respectively. The exchange anisotropy of the BiFeO(3) nanocrystals will shed light on a possible application for magnetism related nanosized devices.

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