Source Mask Optimization (SMO), which enables complex design geometries and improves lithography process window, is a key technology utilized in our hard disk drive (HDD) manufacturing. In this co-optimization technique, mask optimization has been extensively explored and well established in our community. However, source optimization of equal importance receives less attention even with a long history of development since 90s. In the source optimization, the light intensity from a source is generally perceived as a linear addition of intensity from each point source based on Abbe’s method. The drawback on this common approach is that the computation is intensive and timeconsuming as it involves the convolution between the pupil function modulated by each point source and the input mask. Here we present an algorithm of calculating transmission cross coefficients (TCC) for each point source in an illumination source. By following the Hopkins’s approach, the source TCC matrix can be interpreted as the summation of individual TCCs of point sources. Compared to the Abbe’s source formulation, the proposed algorithm provides a simpler, faster and accurate source representation, which is believed to be beneficial to the source mask optimization.
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.
Overlay and alignment target recovery process is often required in recording head fabrication where targets are covered by opaque materials. In this study, we have investigated the recovery process impact on the image based overlay (IBO) measurement performance in critical stages of the recording head fabrication. The trench topography created by the target recovery process can result in the asymmetric resist coating uniformity across the wafer and result in errors in the measured overlay values and modeled correctable wafer terms such as the scale and rotation. These errors become significant at critical pattering layers when there is a large z-spacing between the current resist overlay mark and the previous overlay reference mark layer. The recovery pattern size and the recovery depth impact on the measured overlay performance are evaluated. The overlay mark needs to be optimized to reduce the overlay measurement variation. Overlay mark designs, including box-in-box, AIMid and multi AIMid overlay marks, are investigated. Self-referencing marks (SRM) are used to evaluate recovery process and overlay mark impacts on overlay measurement accuracy.
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