Research On Intelligent Temperature Control Of BP Neural Network For Molecular Beam Epitaxy System

In order to improve the effect of molecular beam epitaxy(MBE)system for growing semiconductor thin film materials in ultra-high vacuum environment,this paper studies the growth temperature control in molecular beam epitaxy system.MBE technology is one of the key technologies for the preparation of new materials for highprecision detectors,smart chips,semiconductor lasers and other high-performance devices,and temperature is a key factor affecting the growth quality of MBE epitaxial layers.In order to grow high-quality epitaxial materials,temperature control in the MBE system plays a particularly important role.Common semiconductor thin film materials require a growth temperature between 200℃ and 1200℃,but traditional temperature control algorithms will have some problems during temperature switching,such as overshoot,delay,and error.The traditional temperature control algorithm represented by the PID algorithm is more and more difficult to meet the requirements of the material growth of the current MBE system.Therefore,through the research and analysis of the MBE system,this paper combines the artificial intelligence algorithm with its temperature control algorithm to meet higher practical requirements.Aiming at the growth temperature control characteristics of MBE system,this paper proposes a temperature control algorithm of MBE system based on BP neural network combined with PID control algorithm,which is referred to as MBE system based on BP-PID.The BP-PID algorithm has a good performance in complex nonlinear systems,and it fits with the MBE system,which can solve the problems of insufficient temperature accuracy and slow response speed in PID control.This paper firstly introduces the basic structure and working principle of the MBE system,and further studies and analyzes the heating process,growth mechanism and temperature control of the system.Then the PID algorithm and the BP neural network algorithm are introduced,and the realization process of the BP neural network algorithm is emphasized.Finally,the working principle of the BP-PID algorithm combined with the two is analyzed.Finally,the MBE system based on BP-PID algorithm is demonstrated in terms of software and hardware.In terms of software,firstly,the mathematical model of the MBE system is calculated by the soaring curve method;secondly,the BP-PID model of the MBE system is built,which includes determining the structure of each layer of the BP neural network and the data collection of the MBE system.The preprocessed data is classified,which can train and test the BP-PID model more effectively,and finally analyze the test results.In terms of hardware,a hardware experiment platform can be built by the STM32F1 controller,MAX6675 thermocouple temperature measurement circuit,computer,and the Eurotherm thermometer and power supply system in the heating device of the MBE system,which verifies the small overshoot and fast response of the BP-PID algorithm.advantage.Through the comparative analysis of BP-PID and PID algorithm,it is concluded that BP-PID algorithm has stronger robustness,more accurate temperature control,shorter response time and faster response speed in MBE system.Therefore,the BP-PID algorithm is more suitable for the MBE system,so that the semiconductor thin film can be grown at the optimal temperature,which ensures the quality of the MBE grown material.