Institute of Microelectronics, Tsinghua University, Beijing, 100084 China.
IEEE Trans Image Process. 2011 Oct;20(10):2856-64. doi: 10.1109/TIP.2011.2131668. Epub 2011 Mar 24.
Source and mask optimization (SMO) has been proposed recently as an effective solution to extend the lifespan of conventional 193 nm lithography, although the process is computationally intensive. In this study, we propose a highly effective and efficient method for source optimization and improve a previous method for mask optimization. An SMO framework is implemented by integrating them. Based on pixel-based source and mask representation, the gradients of the objective function are utilized to guide optimization. In addition to maintain the image fidelity, extra penalties are added into the objective function to increase the depth of focus (DOF) and regularize the source and mask patterns. In our SMO framework, a specially designed mask optimization procedure is performed to enhance the algorithm robustness. Afterward, the source optimization and mask optimization are performed alternatively. Convergence results can be acquired using only two or three iteration cycles. This method is demonstrated using two mask patterns with critical dimensions of 45 nm, including a periodic array of contact holes and a cross gate design. The results show that our method can provide great improvements in both image quality and DOF. The robustness of our method is also verified using different initial conditions.
源掩模优化（SMO）最近被提出作为一种有效解决方案，以延长传统 193nm 光刻的寿命，尽管该过程计算量很大。在这项研究中，我们提出了一种高效的源优化方法，并改进了以前的掩模优化方法。通过将它们集成，实现了一个 SMO 框架。基于基于像素的源和掩模表示，利用目标函数的梯度来指导优化。除了保持图像保真度外，还在目标函数中添加了额外的惩罚项，以增加景深（DOF）并正则化源和掩模图案。在我们的 SMO 框架中，执行了特别设计的掩模优化过程，以增强算法的鲁棒性。然后，交替进行源优化和掩模优化。只需两到三个迭代周期即可获得收敛结果。使用具有 45nm 关键尺寸的两个掩模图案（包括周期性接触孔阵列和十字门设计）演示了该方法。结果表明，我们的方法可以在图像质量和 DOF 方面都提供很大的改进。还使用不同的初始条件验证了我们方法的鲁棒性。
