Research And Application Of Quantum Dot Spin VCSELs In Optical Neural Network

Artificial neural networks can learn,combine and analyze a large amount of information quickly and efficiently,and it greatly improves the efficiency of machine learning.Nowadays,as one of the emerging technologies,it is widely used in all walks of life because of its intelligent and convenient characteristics.Especially in the political,economic and educational industries,the use of artificial neural networks is occupied a very heavy proportion.However,traditional electronic architectures such as central processing unit,graphical processing unit,application-specific integrated circuit and field programmable gate array have some problems in the process of implementing neural networks,such as low efficiency,long time and high energy consumption.The optical neural networks have advantages of high computational speed,high computational accuracy and ultra-low power consumption,so they are highly innovative and practical.Because the optical signal has the advantages of low power consumption,high speed,high bandwidth and high parallelism,it is possible to realize the nonlinear part in the artificial neural networks based on photonic technology,and break through the bottleneck of the traditional nonlinear function based on electronic technology.Many nonlinear functions based on photonic technology have been implemented and published.Compared with the traditional nonlinear function implementation based on electronic technology,linear transformations and some nonlinear changes in the photon networks can be carried out at the speed of more than 100 GHz,which greatly improves the calculation speed and work efficiency.With the study of different types of semiconductor lasers under different structural parameters,we can thoroughly analyze and master the output characteristics of semiconductor lasers,which is conducive to the optimization and application in the research.In recent years,the research progress of semiconductor lasers characteristics and the demand of high speed and low power nonlinear calculation will further promote nonlinear function based on lasers to replace traditional nonlinear function.Therefore,the optical nonlinear function realization with high speed and low power consumption can be completed by combining the high quality nonlinear output characteristics of the lasers with the realization of the nonlinear part in the artificial neural networks.Therefore,this paper studies the implementation of a new optical nonlinear function based on the nonlinear output characteristics of QD spin-VCSELs,and it is applied in optical neural networks.The main contents are as follows:Firstly,the advantages and disadvantages of common activation functions are analyzed from the aspects of the amount of calculation,whether the output mean is 0,and whether it will cause the death of neurons in the process of derivation of back propagation.Therefore,the activation function to be simulated in this paper is determined as Re LU.Then this paper infers the QD spin-VCSELs normalized rate equations,and uses Fourth-Order Runge-Kutta algorithm to solve the nonlinear relationship between the normalized pump intensity and the total output intensity.Then,the nonlinear relationship is put forward to achieve nonlinear unit Re LU in optical neural networks.In the process of selecting the QD spin-VCSELs parameters,the influence of pump polarization,linewidth enhancement factor and normalized gain coefficient on the nonlinear relationship is discussed,and this paper selects a set of optimal structure parameters.Under this parameter,the nonlinear relationship based on QD spin-VCSELs realizes QDRe LU function.For the problem of how to use QDRe LU to simulate Re LU function,a solution of compensating optical pumping for QD spin-VCSELs is proposed.Secondly,the channel attention mechanism is introduced,and the Squeeze-and-Excitation model is introduced.At the same time,it’s mathematical principle is analyzed.Finally,QDRe LU function is applied to artificial neural networks,in order to prove whether the nonlinear function is more feasible than activation function Re LU.At the same time,the Squeeze-and-Excitation model is introduced into the neural network.It’s shown from the results that QDRe LU not only makes full use of the advantages of photon technology such as high speed,low power consumption and high parallelism,but also ensures the accuracy of the whole artificial neural networks.The research content of this paper provides unique research significance and application value for the strategic goal of all-optical neural networks.