本文關(guān)鍵詞：基于改進(jìn)粒子群算法的數(shù)字光刻成像研究　出處：《南昌航空大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 數(shù)字光刻 圖形失真 掩模優(yōu)化 改進(jìn)的粒子群算法 成像算法

【摘要】：集成電路是當(dāng)今智能化產(chǎn)業(yè)的基礎(chǔ),而光刻技術(shù)是集成電路發(fā)展的主要推動(dòng)力。相較于傳統(tǒng)的掩模光刻技術(shù),數(shù)字光刻技術(shù)具有靈活、速度快、無需物理掩模等優(yōu)點(diǎn)。然而,由于光學(xué)鄰近效應(yīng)的存在,掩模圖形投影到硅片上時(shí)會(huì)出現(xiàn)畸變,造成掩模失真。本文圍繞提高數(shù)字光刻質(zhì)量進(jìn)行了深入研究,主要內(nèi)容如下:(1)研究基于像素的數(shù)字光刻成像方法。為了提高數(shù)字光刻的質(zhì)量,我們就必須找出更好的掩模。運(yùn)用傳統(tǒng)的成像算法,不僅計(jì)算復(fù)雜、成像精度低,并且傳統(tǒng)的成像算法把掩模作為整體進(jìn)行優(yōu)化,不利于分析每個(gè)像素對(duì)光刻成像的影響�；谙袼氐臄�(shù)字光刻技術(shù)使用聚焦光學(xué)器件投影圖像,因此單個(gè)像素的強(qiáng)度分布可以被認(rèn)為是一個(gè)點(diǎn)擴(kuò)散函數(shù),其可以由高斯分布描述。通過仿真實(shí)驗(yàn),證明了該算法具有更快的成像速度和更高的成像精度。(2)提出了一種基于雙重循環(huán)增加粒子多樣性的改進(jìn)粒子群算法。粒子群算法具有收斂快、易實(shí)現(xiàn)的優(yōu)點(diǎn)。但是,傳統(tǒng)的粒子群算法對(duì)于復(fù)雜的問題很有可能會(huì)陷入局部極值,從而導(dǎo)致過早收斂,無法獲得最優(yōu)解,而導(dǎo)致陷入局部極值的主要原因之一是算法后期粒子多樣性的缺失,為了避免陷入局部極值,我們?cè)O(shè)置兩重循環(huán)來增加粒子的多樣性。第一重循環(huán)控制粒子群重新初始化的次數(shù),以此來增加粒子群的多樣性,相應(yīng)的擴(kuò)大搜索范圍;第二重循環(huán)控制每次粒子群更新之后的粒子的更新次數(shù)。將改進(jìn)的粒子群算法與其它幾種粒子群算法在多個(gè)測(cè)試函數(shù)上進(jìn)行了實(shí)驗(yàn)對(duì)比,結(jié)果證明該算法具有更好的優(yōu)化性能。(3)利用改進(jìn)的粒子群算法對(duì)數(shù)字光刻掩模進(jìn)行優(yōu)化。由于基于像素的數(shù)字光刻成像算法會(huì)使與照明圖像無關(guān)的像素也獲得光強(qiáng),進(jìn)而會(huì)導(dǎo)致成像圖形邊緣比較模糊�；诖�,本文對(duì)掩模上像素灰度值進(jìn)行優(yōu)化。利用改進(jìn)的粒子群算法先對(duì)灰度因子進(jìn)行初始化,將其作為當(dāng)前全局最優(yōu)。其次對(duì)灰度因子進(jìn)行更新,并將成像結(jié)果與原始掩模進(jìn)行對(duì)比,若圖形誤差小于當(dāng)前全局最優(yōu)對(duì)應(yīng)的圖形誤差,則全局最優(yōu)更新,否則保持不變,通過不斷迭代,最終找到合適的灰度因子,從而改變成像圖像光強(qiáng)分布,提高成像質(zhì)量。最后對(duì)其在不同的掩模圖形上進(jìn)行了實(shí)驗(yàn),證明了算法的優(yōu)化性能。
[Abstract]:Integrated circuit is the basis of intelligent industry nowadays, and lithography technology is the main driving force of the development of integrated circuit. Compared with traditional mask lithography technology, digital lithography technology is flexible and fast. However, because of the optical proximity effect, the mask pattern will be distorted when projected onto the silicon wafer, resulting in mask distortion. In this paper, the improvement of digital lithography quality is studied in depth. In order to improve the quality of digital lithography, we must find a better mask. Using traditional imaging algorithm, not only computational complexity. The traditional imaging algorithm optimizes the mask as a whole, which is not conducive to analyzing the effect of each pixel on the lithography. The pixel based digital lithography technology uses focused optical devices to project images. Therefore, the intensity distribution of a single pixel can be considered as a point diffusion function, which can be described by Gao Si distribution. It is proved that the algorithm has faster imaging speed and higher imaging accuracy.) an improved particle swarm optimization algorithm based on double cycles to increase particle diversity is proposed. The particle swarm optimization algorithm has fast convergence. However, the traditional particle swarm optimization algorithm is likely to fall into local extremum for complex problems, which leads to premature convergence and can not obtain the optimal solution. One of the main reasons for falling into local extremum is the loss of particle diversity in the later stage of the algorithm, in order to avoid falling into local extremum. We set up double cycles to increase the diversity of particles. The first cycle controls the times of particle swarm reinitialization to increase the diversity of particle swarm and expand the search range. The second cycle controls the number of particle updates after each PSO update. The improved PSO algorithm is compared with other PSO algorithms on several test functions. The results show that the algorithm has better optimization performance. The improved particle swarm optimization algorithm is used to optimize the digital lithography mask. Because of the pixel based digital lithography imaging algorithm, the pixels independent of the illumination image can also get light intensity. Based on this, the gray value of pixels on the mask is optimized, and the improved particle swarm optimization algorithm is used to initialize the gray level factor. It is regarded as the current global optimal. Secondly, the gray factor is updated, and the imaging results are compared with the original mask. If the image error is smaller than the current global optimal corresponding graphics error, then the global optimal update. Otherwise, we can find the appropriate gray factor through iteration, which can change the intensity distribution of the imaging image and improve the image quality. Finally, the experiment is carried out on different mask images. The optimization performance of the algorithm is proved.
【學(xué)位授予單位】：南昌航空大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18;TN405

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