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Self-aligned double patterning (SADP) is one of the most promising techniques for sub-20nm technology. Spacer-is-dielectric SADP using a cut process is getting popular because of its higher design flexibility; for example, it can decompose odd cycles without the need of inserting any stitch. This paper presents the first work that applies the cut process for decomposing odd cycles during routing. For SADP, further, overlay control is a critical issue for yield improvement; while published routers can handle only partial overlay scenarios, our work identifies all the scenarios that induce overlays and proposes a novel constraint graph to model all overlays. With the developed techniques, our router can achieve high-quality routing results with significantly fewer overlays (and thus better yields). Compared with three state-of-the-art studies, our algorithm can achieve the best quality and efficiency, with zero cut conflicts, smallest overlay length, highest routability, and fastest running time.

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A zoo of deep nets is available these days for almost any given task, and it is increasingly unclear which net to start with when addressing a new task, or which net to use as an initialization for fine-tuning a new model. To address this issue, in this paper, we develop knowledge flow which moves 'knowledge' from multiple deep nets, referred to as teachers, to a new deep net model, called the student. The structure of the teachers and the student can differ arbitrarily and they can be trained on entirely different tasks with different output spaces too. Upon training with knowledge flow the student is independent of the teachers. We demonstrate our approach on a variety of supervised and reinforcement learning tasks, outperforming fine-tuning and other 'knowledge exchange' methods. INTRODUCTIONResearch communities have amassed a sizable number of deep net architectures for different tasks, and new ones are added almost daily. Some of those architectures are trained from scratch while others are fine-tuned, i.e., before training, their weights are initialized using a structurally similar deep net which was trained on different data.

Multiple e-beam lithography (MEBL) is one of the most promising next generation lithography (NGL) technologies for high volume manufacturing, which improves the most critical issue of conventional single e-beam lithography, throughput, by simultaneously using thousands or millions of e-beams. For parallel writing in MEBL, a layout is split into stripes and patterns are cut by stripe boundaries, which are defined as stitching lines. Critical patterns cut by stitching lines could suffer from severe pattern distortion or even yield loss. Therefore, considering the positions of stitching lines and avoiding stitching line-induced bad patterns are required during layout design. In this paper, we propose the first work of stitch-aware routing framework for MEBL based on a two-pass bottom-up multilevel router. We first identify three types of stitching line-induced bad patterns which should not exist in an MEBL-friendly routing solution. Then, stitch-aware routing algorithms are respectively developed for global routing, layer/track assignment and detailed routing. Experimental results show that our stitch-aware routing framework can effectively reduce stitching line-induced bad patterns and thus may not only improve the manufacturability but also facilitate the development of MEBL.