Predicting substrate-induced focus error

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The ever-shrinking lithography process window dictates that we maximize our process window, minimize process variation, and quantify the disturbances to an imaging process caused upstream of the imaging step. Relevant factors include across-wafer and wafer-to-wafer film thickness variation, wafer flatness, wafer edge effects, and design-induced topography. We present our effort to predict design-induced focus error hot spots based on prior knowledge of the wafer surface topography. This knowledge of wafer areas challenging the edge of our process window enables a constructive discussion with our design and integration team to prevent or mitigate focus error hot spots upstream of the imaging process.

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To reduce chip-scale topography variation, dummy fill is commonly used to improve the layout density uniformity. Previous works either sought the most uniform density distribution or sought to minimize the inserted dummy fills while satisfying certain density uniformity constraint. However, due to more stringent manufacturing challenges, more criteria, like line deviation and outlier, emerge at newer technology nodes. This article presents a joint optimization scheme to consider variation, total fill, line deviation, outlier, overlap, and running time simultaneously. More specifically, first we decompose the rectilinear polygons and partition fillable regions into rectangles for easier processing. After decomposition, we insert dummy fills into the fillable rectangular regions optimizing the fill metrics simultaneously. We propose three approaches, Fast Median approach, LP approach, and Iterative approach, which are much faster with better quality, compared with the results of the top three contestants in the ICCAD Contest 2014.
We introduce the fill optimization problem and benchmarks. We provide two new hotspot definitions, slot line deviation and outliers, both of which pertain to yield. We provide the inputs, expected output, as well as objectives and constraints of the problem.
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