Deep Learning-Driven Simultaneous Layout Decomposition and Mask Optimization

Combining multiple pattern lithography (MPL) and optical proximity correlation (OPC) pushes the limit of 193nm wavelength lithography to go further. Considering that layout decomposition may generate plenty of solutions with diverse printabilities, relying on conventional mask optimization process to select the best candidates for manufacturing is computationally expensive. Therefore, an accurate and efficient printability estimation is crucial and can significantly accelerate the layout decomposition and mask optimization (LDMO) process. In this paper, we propose a CNN based prediction and integrate it into our new high performance LDMO framework. We also develop both the layout and the decomposition sampling strategies to facilitate the network training. The experimental results demonstrate the effectiveness and the efficiency of the proposed algorithms.