Research On Digital Photolithography Based On Improved Particle Swarm Optimization

Integrated circuits are the foundation of today's intelligent industries,and lithography is the key technology of integrated circuits.Compared to the traditional mask lithography technology,digital lithography technology has the advantages of flexibility,speed,no physical mask and so on.However,due to the presence of optical proximity effects,distortion occurs when the mask pattern is projected onto the silicon wafer,causing mask distortion.This paper focuses on improving the quality of digital lithography,the main contents are as follows:(1)Research the digital lithography imaging method based on pixels.In order to improve the quality of digital printing,we must find out the better mask.Using the traditional imaging algorithm,not only the calculation is complex,the imaging accuracy is low,and the traditional imaging algorithm to optimize the mask as a whole,is not conducive to the analysis of each pixel on the impact of lithography imaging.The pixel-based stereoscopic lithography system uses a focused optics to project images.Therefore,the intensity distribution of a pixel can be considered as a point spread function,which can be described by a Gaussian distribution.The simulation results show that the algorithm has faster imaging speed and higher imaging accuracy.(2)An improved particle swarm algorithm is proposed.Particle swarm optimization(PSO)has the advantages of Convergence fast,easy implementation.However,the traditional particle swarm algorithm is likely to fall into the local extremum for complex problems,leading to premature convergence and lack of optimal solution.Therefore,we optimized the traditional particle swarm algorithm.One of the main reasons for the traditional particle swarm algorithm to fall into the local extremum is the lack of particle diversity,in order to jump out of the local extremum,we set the double cycle to increase the diversity of the particles.The first cycle controls PSO to initialize the number of times,the second cycle controls the number of times of particles updates after each particle population initialization.Theimproved particle swarm optimization algorithm is compared with the other particle swarm algorithm in several test functions.The results show that the algorithm has better optimization performance.(3)The improved particle swarm optimization algorithm is used to optimize the digital lithography mask.Since the pixel-based digital photolithography imaging algorithm results in light intensity that is not associated with the illumination image,the edge of the imaging pattern is blurred.Based on this,this paper optimizes the pixel gray value on the mask.The improved particle swarm algorithm is used to initialize the gray scale factor as the current global optimal.Secondly,the gray scale factor is updated and the imaging result is compared with the original mask.If the graph error is less than the current global optimal correspondence of the graphical error,the global optimal update,otherwise remain unchanged.Through continuous iteration,and finally find the appropriate gray scale factor,thus changing the image intensity distribution,improve image quality.Finally,it is experimented on different mask patterns,which proves that it can get better imaging results.