Pan Yihua, Ma Xu, Zhang Shengen, Garcia-Frias Javier, Arce Gonzalo R
Appl Opt. 2021 Sep 20;60(27):8307-8315. doi: 10.1364/AO.433962.
Source and mask optimization (SMO) is a widely used computational lithography technology that greatly improves the image fidelity of lithography systems. This paper develops an efficient informatics-based SMO (EISMO) method to improve the image fidelity of lithography systems. First, a communication channel model is established to depict the mechanism of information transmission in the SMO framework, where the source is obtained from the gradient-based SMO algorithm. The manufacturing-aware mask distribution is then optimized to achieve the best mutual information, and the theoretical lower bound of lithography patterning error is obtained. Subsequently, an efficient informatics-based method is proposed to refine the mask optimization result in SMO, further reducing the lithography patterning error. It is shown that the proposed EISMO method is computationally efficient and can achieve superior imaging performance over the conventional SMO method.
源与掩模优化（SMO）是一种广泛应用的计算光刻技术，它极大地提高了光刻系统的图像保真度。本文开发了一种基于信息学的高效SMO（EISMO）方法，以提高光刻系统的图像保真度。首先，建立一个通信信道模型来描述SMO框架中的信息传输机制，其中源是从基于梯度的SMO算法获得的。然后优化考虑制造因素的掩模分布以实现最佳互信息，并获得光刻图案化误差的理论下限。随后，提出一种基于信息学的高效方法来细化SMO中的掩模优化结果，进一步降低光刻图案化误差。结果表明，所提出的EISMO方法计算效率高，并且与传统的SMO方法相比可以实现更优异的成像性能。
