Prediction model of K₂CsSb photocathode reflectivity based on LSTM

Aiming at the problem that the growth state of K₂CsSb photocathode cannot be predicted in the current preparation process of K₂CsSb photocathode, a prediction model of K₂CsSb photocathode reflectivity based on long short-term memory (LSTM) recurrent neural network was proposed. The one-dimensional original reflectivity data set was reconstructed into a two-dimensional data input model after cleaning, screening, serialization and other preprocessing methods. In order to make full use of the highly correlated characteristics of reflectivity data in time series, this model used a double-layer LSTM network to extract features, the prediction results were output through the fully connected layer, and the mean square error (MSE) was used as the evaluation standard for the prediction effect of the model. The experimental results show that the network structure of the model is reasonable and performs well in different data sets, and the prediction accuracy rate can reach 99.21%. The proposed model can be used in the fabrication process of K₂CsSb photocathode, and the process parameters can be adjusted by feedback of the reflectivity prediction value to approach the target trend, which can promote the performance of the photocathode.