Browsing by Author "Chang, Wei-Chun"

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Efficient Search of Layout Hotspot Patterns for Matching SEM Images using Multilevel Pixelation
(2019-01-01)
Tseng, Sean Shang-En
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Chang, Wei-Chun
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Jiang, Iris Hui-Ru
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Zhu, Jun
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Shiely, James P.
Layout features become highly susceptible to lithography process fluctuations due to the widening subwavelength lithography gap. Problematic layout patterns incur poor printability even if they pass design rule checking. These hotspots should be detected and removed at early design phases to improve manufacturability. While existing studies mainly focus on hotspot detection and pattern classification, hotspot pattern library generation is rarely addressed in literature but crucial to the effectiveness and efficiency of hotspot detection. For an advanced process, in addition to yield-limiting patterns inherent from old processes and computation intensive lithography simulation, defect silicon images (SEM images) inspected from test wafers provide more realistic process-dependent hotspots. For facilitating hotspot pattern library generation, we raise a pattern matching problem of searching design layout patterns that may induce problematic SEM images. The key challenge is the various shape distortions between an SEM image and corresponding design layouts. Directly matching either feature points or shapes of both is thus not applicable. We observe that even with shape distortions, matched design layouts and the SEM image have similar density distribution. Therefore, in this paper, we propose an efficient multilevel pixilation framework to seek layout clips with similar density distribution from coarse- to fine-granularities to an SEM image. The proposed framework possesses high parallelism. Our results show that the proposed method can effectively and efficiently identify matched layout pattern candidates.
Eye On You: Fusing Gesture Data from Depth Camera and Inertial Sensors for Person Identification
(2018-01-01)
Chang, Wei-Chun
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Wu, Cheng-Wei
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Tsai, Richard Yi-Chia
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Lin, Kate Ching-Ju
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Tseng, Yu-Chee
Person identification (PID) is a key issue in many IoT applications. It has long been studied and achieved by technologies such as RFID and face/fingerprint/iris recognition. These approaches, however, have their limitations due to environmental constraints (such as lighting and obstacles) or require close contact to specific devices. Therefore, their recognition rates highly depend on use scenarios. To enable reliable and remote PID, in this work, we present EOY (Eye On You) 1, a data fusion approach that combines two kinds of sensors, a 3D depth camera and wearable sensors embedded with inertial measurement unit (IMU). Since these two kinds of data share common features, we are able to fuse them to conduct PID. Further, the result can be transferred to a mobile platform (such as robot) since we have less constraints on devices. To realize EOY, we develop fusion algorithms to address practical challenges, such as asynchronous timing and coordinate calibration. The experimental evaluation shows that EOY can achieve the recognition rate of 95% and is very robust even in crowded areas.
iClaire: A Fast and General Layout Pattern Classification Algorithm
(2017-01-01)
Chang, Wei-Chun
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Jiang, Iris Hui-Ru
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Yu, Yen-Ting
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Liu, Wei-Fang
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電子工程學系及電子研究所
Layout pattern classification, which groups similar layout clips into clusters, underlies a variety of design for manufacturability (DFM) applications such as hotspot library generation, hierarchical data storage, and yield optimization speedup. The key challenges of layout pattern classification are clip representation and clip clustering, while the mutually conflicting concerns are efficiency and solution quality (in terms of cluster count). In this paper, we present a fast and general layout pattern classification algorithm. Our simple but general clip representation captures both topology and density; we can handle not only rigid area match or edge displacement constraints but also variant edge tolerances and don't care regions. On the other hand, for achieving a small cluster count, our clip clustering is guided by the natural grouping structure of layout clips. Our experiments are conducted on 2016 CAD contest at ICCAD benchmark suite; our results show that our algorithm outperforms the reference solution and all contest winning teams, delivering the smallest cluster count, fastest runtime, and 100% validity. In addition to the good solution quality, the interplay between adopted simple and easily manipulated data structures and our algorithm makes it fast and viable to be incorporated into practical DFM flows.