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As Double Patterning Lithography(DPL) becomes the leading candidate for sub-30nm lithography process, we need a fast and lithography friendly decomposition framework. In this paper, we propose a multi-objective min-cut based decomposition framework for stitch minimization, balanced density, and overlay compensation, simultaneously. The key challenge of DPL is to accomplish high quality decomposition for large-scale layouts under reasonable runtime with the following objectives: a) the number of stitches is minimized, b) the balance between two decomposed layers is maximized for further enhanced patterning, c) the impact of overlay on coupling capacitance is reduced for less timing variation. We use a graph theoretic algorithm for minimum stitch insertion and balanced density. An additional decomposition constraints for self-overlay compensation are obtained by integer linear programming(ILP). With the constraints, global decomposition is executed by our modified FM graph partitioning algorithm. Experimental results show that the proposed framework is highly scalable and fast: we can decompose all 15 benchmark circuits in five minutes in a density balanced fashion, while an ILP-based approach can finish only the smallest five circuits. In addition, we can remove more than 95% of the timing variation induced by overlay for tested structures.

ABSTRACT3D integration has new manufacturing and design challenges such as timing corner mismatch between tiers and device variation due to Through Silicon Via (TSV) induced stress. Timing corner mismatch between tiers is caused because each tier is manufactured in independent process. Therefore, inter-die variation should be considered to analyze and optimize for paths spreading over several tiers. TSV induced stress is another challenge in 3D Clock Tree Synthesis (CTS). Mobility variation of a clock buffer due to stress from TSV can cause unexpected skew which degrades overall chip performance. In this paper, we propose clock tree design methodology with the following objectives: (a) to minimize clock period variation by assigning optimal zlocation of clock buffers with an Integer Linear Program (ILP) formulation, (b) to prevent unwanted skew induced by the stress. In the results, we show that our clock buffer tier assignment reduces clock period variation up to 34.2%, and the most of stress-induced skew can be removed by our stress-aware CTS. Overall, we show that performance gain can be up to 5.7% with our robust 3D CTS.

Under real and continuously improving manufacturing conditions, lithography hotspot detection faces several key challenges. First, real hotspots become less but harder to fix at post-layout stages; second, false alarm rate must be kept low to avoid excessive and expensive post-processing hotspot removal; third, full chip physical verification and optimization require fast turn-around time. To address these issues, we propose a high performance lithographic hotspot detection flow with ultra-fast speed and high fidelity. It consists of a novel set of hotspot signature definitions and a hierarchically refined detection flow with powerful machine learning kernels, ANN (artificial neural network) and SVM (support vector machine). We have implemented our algorithm with industry-strength engine under real manufacturing conditions in 45nm process, and showed that it significantly outperforms previous state-of-the-art algorithms in hotspot detection false alarm rate (2.4X to 2300X reduction) and simulation run-time (5X to 237X reduction), meanwhile archiving similar or slightly better hotspot detection accuracies. Such high performance lithographic hotspot detection under real manufacturing conditions is especially suitable for guiding lithography friendly physical design.