With the exploration of ferroelectric materials, researchers have a strong desire to explore the next generation of non‐volatile ferroelectric memory with silicon‐based epitaxy, high‐density storage, and algebraic operations. Herein, a silicon‐based memristor with an epitaxial vertically aligned nanostructures BaTiO3–CeO2 film based on La0.67Sr0.33MnO3/SrTiO3/Si substrate is reported. The ferroelectric polarization reversal is optimized through the continuous exploring of growth temperature, and the epitaxial structure is obtained, thus it improves the resistance characteristic, the multi‐value storage function of five states is achieved, and the robust endurance characteristic can reach 109 cycles. In the synapse plasticity modulated by pulse voltage process, the function of the spiking‐time‐dependent plasticity and paired‐pulse facilitation is simulated successfully. More importantly, the algebraic operations of addition, subtraction, multiplication, and division are realized by using fast speed pulse of the width ≈50 ns. Subsequently, a convolutional neural network is constructed for identifying the CIFAR‐10 dataset, to simulate the performance of the device; the online and offline learning recognition rate reach 90.03% and 92.55%, respectively. Overall, this study paves the way for memristors with silicon‐based epitaxial ferroelectric films to realize multi‐value storage, algebraic operations, and neural computing chip applications.
In this work, a memristor device with Pd/HfO2:Gd/La0.67Sr0.33MnO3/SrTiO3/Si was prepared, and its synaptic behavior was investigated. The memristor shows excellent performance in I–V loops and ferroelectric properties. Through polarization, the conductance modulation of the memristor is achieved by the reversal of the ferroelectric domain. In addition, we simulate biological synapses and synaptic plasticities such as spike-timing-dependent plasticity, paired-pulse facilitation, and an excitatory postsynaptic current. These results lay the foundation for the development of synaptic functions in Hf-based ferroelectric thin films and will promote the development of synaptic applications for neuromorphic computing chips.
Aiming at the problem that the current single-objective control model of parallel pumping groups only focuses on the optimization of energy efficiency of operating conditions and operating costs, and cannot adjust the real-time working conditions of the pump group according to the comprehensive energy efficiency state of the pump group in the whole life cycle and adjust the pump group operation strategy accordingly, A multi-objective optimal control model for energy efficiency of a pump set is proposed, which can adjust the weight coefficients of three objective functions autonomously according to the current energy efficiency state of the pump set in the whole life cycle. In this way, the high energy efficiency of the parallel pump group in the low wear stage, that is, the low target deviation and specific energy consumption, and the high reliability in the high wear stage, that is, the lower impeller load improves the efficiency of the pump group in the whole life cycle and extend the service life of the pump group. Determine the multi-objective energy efficiency optimization control model of the pump group, use the main function linear and geometric weighting method, the ideal point value, and the distance deviation method to determine the objective function, and solve the multi-objective ideal point model with the help of LINGO, and obtain the optimal solution of the highest system total efficiency, the lowest pump group specific energy consumption and the highest system reliability. Pareto Frontier Comparison is used to study the transversality of the ideal point model solution set. Experimental results show that through the distribution of the model solution in the indicator space, the model can adjust the control strategy according to the real-time state of the pump group by adjusting the target weight combination.INDEX TERMS Parallel pumping groups; energy efficiency optimization; full life cycle; multi-objective
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