The application of a new stochastic search algorithm 'Adam' in inverse lithography technology (ILT) in critical recording head fabrication process

In our manufacturing process for the hard disk drive (HDD) recording heads, the home-brew pixelated-based inverse lithography is being employed in some critical lithography layers, providing significant improvement on pattern fidelity and process stability. Generally, the process-aware (defocus and dose) inverse lithography is realized through the stochastic gradient decent (SGD) method. In this paper, a widely used search algorithm Adam is introduced for our inverse lithography framework. The new algorithm utilizes the first and second moments of gradients to adapt the learning rate for each individual pixel during the stochastic searching process. Unlike SGD, such derived learning rate is invariant to the magnitude of gradient. In our experiment, we demonstrated reduced edge placement error (EPE), enlarged process window and tighter critical dimension (CD) distribution with Adam on our test cases of isolated features. We believe that the inverse lithography with Adam algorithm is also applicable to dense features with the similar benefits.