Ma Xu, Han Chunying, Li Yanqiu, Dong Lisong, Arce Gonzalo R
Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of China, School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China.
J Opt Soc Am A Opt Image Sci Vis. 2013 Jan 1;30(1):112-23. doi: 10.1364/JOSAA.30.000112.
Immersion lithography systems with hyper-numerical aperture (hyper-NA) (NA>1) have become indispensable in nanolithography for technology nodes of 45 nm and beyond. Source and mask optimization (SMO) has emerged as a key technique used to further improve the imaging performance of immersion lithography. Recently, a set of pixelated gradient-based SMO approaches were proposed under the scalar imaging models, which are inaccurate for hyper-NA settings. This paper focuses on developing pixelated gradient-based SMO algorithms based on a vector imaging model that is accurate for current immersion lithography. To achieve this goal, an integrative and analytic vector imaging model is first used to formulate the simultaneous SMO (SISMO) and sequential SMO (SESMO) frameworks. A gradient-based algorithm is then exploited to jointly optimize the source and mask. Subsequently, this paper studies and compares the performance of individual source optimization (SO), individual mask optimization (MO), SISMO, and SESMO. Finally, a hybrid SMO (HSMO) approach is proposed to take full advantage of SO, SISMO, and MO, consequently achieving superior performance.
具有超数值孔径（NA>1）的浸没式光刻系统已成为45纳米及以下技术节点纳米光刻中不可或缺的设备。光源与掩膜优化（SMO）已成为用于进一步提高浸没式光刻成像性能的关键技术。最近，在标量成像模型下提出了一组基于像素化梯度的SMO方法，而这些方法在超数值孔径设置下并不准确。本文着重基于对当前浸没式光刻准确的矢量成像模型来开发基于像素化梯度的SMO算法。为实现这一目标，首先使用一个综合分析的矢量成像模型来构建同时进行的SMO（SISMO）和顺序进行的SMO（SESMO）框架。然后利用基于梯度的算法来联合优化光源和掩膜。随后，本文研究并比较了单独的光源优化（SO）、单独的掩膜优化（MO）、SISMO和SESMO的性能。最后，提出了一种混合SMO（HSMO）方法，以充分利用SO、SISMO和MO，从而实现卓越的性能。
