2results about How to "No size limit" patented technology

The application provides a preparation method of a flat epitaxial diamond semiconductor and relates to the technical field of diamond semiconductor preparation. The preparation method comprises the following steps: cleaning a microwave plasma device chamber, pretreating a single crystal diamond substrate, etching the single crystal diamond substrate and preparing a hydrogen-terminated diamond semiconductor, wherein a magnetic field generated by a magnetic excitation coil is used to constrain the microwave plasma in the microwave vapor deposition device, so that carbon atoms are uniformly and rapidly deposited on the single crystal diamond substrate, and finally, a single crystal diamond epitaxial layer with a flat surface is obtained. The preparation method of the application can synergistically optimize the carrier mobility, surface density and square resistance of the hydrogen-terminated diamond semiconductor, is simple and easy to operate, has low cost and low energy consumption, is high in efficiency, and is beneficial to industrial large-scale production and popularization.

The application relates to a material plasticity constitutive modeling method and device, wherein the prediction data set in the method not only contains data obtained by performing a material plasticity deformation behavior experiment, but also contains prediction data obtained based on a phenomenological constitutive model, so that the prediction data can be combined with the experimental data, and then the material parameters are not limited to the scale of the experimental data; double optimization of a neural network model is realized by combining physical loss and data loss, so that when the model is iteratively optimized, the physical information is combined for constraint, the precision of the iterative optimization is improved, and in the optimization process, the parameters of the neural network model are iteratively optimized by combining a simulated annealing algorithm, so that the obtained target neural network model meets the high-precision requirement of material plasticity forming under multiple working conditions.