Ferroelectric (FE) Hf1−xZrxO2 is a potential candidate for emerging memory in artificial intelligence (AI) and neuromorphic computation due to its non-volatility for data storage with natural bi-stable characteristics. This study experimentally characterizes and demonstrates the FE and antiferroelectric (AFE) material properties, which are modulated from doped Zr incorporated in the HfO2-system, with a diode-junction current for memory operations. Unipolar operations on one of the two hysteretic polarization branch loops of the mixed FE and AFE material give a low program voltage of 3 V with an ON/OFF ratio >100. This also benefits the switching endurance, which reaches >109 cycles. A model based on the polarization switching and tunneling mechanisms is revealed in the (A)FE diode to explain the bipolar and unipolar sweeps. In addition, the proposed FE-AFE diode with Hf1−xZrxO2 has a superior cycling endurance and lower stimulation voltage compared to perovskite FE-diodes due to its scaling capability for resistive FE memory devices.
A bridge health monitoring system based on neural network technology is proposed in this paper. Two major ground excitations recorded in Taiwan were used to establish the NARX-based system. Analytical results from different methods including transfer function, ARX-based model, and the proposed neural network-based system were used to evaluate the efficiency in health monitoring. The result shows that the proposed system can be used successfully with superior advantages after major earthquakes for bridge health monitoring.
In this paper, a pseudo-single-degree-of-freedom system identification procedure is developed to investigate the dynamic characteristics of energy-dissipated buildings equipped with symmetric ductile braces (SDBs). The primary structure is assumed to be linear on account of substantial reduction of seismic forces due to the installation of SDBs for which a bilinear hysteretic model is considered. The hysteretic model is in turn characterized by a backbone curve by which the multi-valued restoring force is transformed into a single-valued function. With the introduction of backbone curves, the system identification analysis of inelastic structures is significantly simplified. The proposed algorithm extracts individually the physical parameters of each primary structure and each energy-dissipation device that are considered useful information in the structural health monitoring. A numerical example is conducted to demonstrate the feasibility of using the proposed technique for physical parameter identification of partially inelastic energy-dissipated buildings.
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