頻譜診斷與微顯影平行計算

Spectrum Diagnosis and Parallel Optical Simulation

黃冠儒(Kuan-Lu Huang)

指導教授 ： 陳中平

國立臺灣大學/電機資訊學院/電子工程學研究所/碩士(2010年)

https://doi.org/10.6342/NTU.2010.00190

摘要

為了保證奈米壓印製造光柵的品質，光散(opticalscatterometry) 是一個有效率和有效的方法來診斷實際光柵的幾何形狀。為了方便診斷的過程，一個有效率針對大型資料庫的匹配演算法是非常重要的。在本篇論文中，我們提出一個有效的演算法利用最小誤差(MSE)的方式用來比對大型的頻譜資料庫，藉此反推原始的幾何組態。我們利用奇異值分解(Singular Value Decomposition)對大型的資料庫作壓縮並使用分層的動差(Moment)匹配方式來執行匹配演算法。我們的搜尋和診斷演算法是非常快速且精確的。跟傳統的最小誤差比起來，快上了3000倍以上且精確度在0.1%以內。

第二部分是介紹使用平行計算的方式來加快微顯影中的成像生成。隨者超大型積體電路技術的特徵尺寸(feature size)迅速縮小，已小於曝光光的的波長，光的繞射效應使得曝光後的圖像明顯偏離了原本設計的光罩。因此，微顯影結果的品質，在超大型積體電路(VLSI)的製造過程中是非常重要的。但是往往花費了相當多的時間來產生成像。在論文中，我們使用CUDA技術，它是一個通用的平行計算架構，充分利用在NVIDIA繪圖晶片(GPU)中的平行計算引擎，用來加快微顯影中的圖像生成。

關鍵字

光散射 ； 奇異值分解 ； 動差匹配 ； 阿貝成像方法 ； 繪圖處理器平行運算

並列摘要

To ensure the quality of the nanoprint fabricated optical gratings, optical scatterometry (OS) is an efficient and effective mean to diagnose the actual fabricated geometry. To facilitate the diagnosis process, efficient pattern matching algorithms over a huge database are of great importance.

In this thesis, we propose an efficient algorithm using minimum error square approach used to matching in a huge simulated spectrum database in order to obtain the original geometric configuration inversely.We use Singular Value Decomposition to do compression on large database and the use of hierarchical moment to perform matching algorithm; our searching and diagnosis algorithm is extremely fast and accurate. It is over 3000x faster than a exhausted searching algorithm within 0.1% accuracy.
The second part is to introduce the use of parallel computing in the imaging of microlithography for acceleration. As the VLSI technology feature sizes quickly shrink smaller than the wavelength of exposure light sources, the diffraction effects have made the exposed patterns significantly deviated from the original intended mask pattern. Therefore, the quality of microlithography simulation is an important part of the VLSI manufacturing process. However, it takes considerable time to produce image. In the thesis, we use CUDA, which is a general purpose parallel computing architecture that leverages the parallel compute engine in NVIDIA graphics processing units (GPUs) to speed up the image generation in Microlithography simulation.

並列關鍵字

Optical Scatterometry, Singular Value Decomposition ； Moment Matching ； Abbe’s method ； CUDA

參考文獻

[2] R. Antos, J. Pistora, I. Ohlidal, K. Postava, J. Mistrik, T. Yamaguchi, S. Visnovsky, and M. Horie, “Specular spectroscopic ellipsometry for the critical dimension monitoring of gratings fabricated on a thick transparent plate,” J. Appl. Phys. 97, 053107 (2005).

[3] H. Huang and F. L. Terry, “Spectroscopic ellipsometry and reflectometry from gratings (scatterometry) for critical dimension measurement and in situ, real-time process monitoring,” Thin Solid Films 455, 828–836 (2004).

[4] C. J. Raymond, M. R. Murnane, S. S. H. Naqvi, and J. R. McNeil, “Metrology of subwavelength photoresist gratingsusing optical scatterometry,” J. Vac. Sci. Technol. B 13, 1484–1495 (1995).

[5] J. Garnaes, P. E. Hansen, N. Agersnap, J. Holm, F. Borsetto, and A. Kühle, “Profiles of a high-aspect-ratio grating determined by spectroscopic scatterometry and atomic-force microscopy,” Appl. Opt. 45, 3201–3212 (2006).

[6] R. H. Krukar, S. L. Prins, D. M. Krukar, G. A. Petersen, S. M. GasparGaspar, J. R. McNeil, S. S. H. Naqvi, and D. R. Hush, “Using scattered light modeling for semiconductor critical dimension metrology and calibration,” Proc. SPIE 1926, 60–71 (1993).