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Lithography Hotspot Detection via Heterogeneous Federated Learning with Local Adaptation [article]

Xuezhong Lin, Jingyu Pan, Jinming Xu, Yiran Chen, Cheng Zhuo
<span title="2021-07-30">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we propose a heterogeneous federated learning framework for lithography hotspot detection that can address the aforementioned issues.  ...  As technology scaling is approaching the physical limit, lithography hotspot detection has become an essential task in design for manufacturability.  ...  CONCLUSION In this paper, we propose a novel heterogeneous federated learning based hotspot detection framework with local adaptation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.04367v3">arXiv:2107.04367v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jyaif55xmrar3gsxlzr345m32u">fatcat:jyaif55xmrar3gsxlzr345m32u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210809101353/https://arxiv.org/pdf/2107.04367v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d3/6e/d36ebea75e7151b35245f3a588c6c7fc61fc3a4a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.04367v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

VLSI Mask Optimization: From Shallow To Deep Learning [article]

Haoyu Yang, Wei Zhong, Yuzhe Ma, Hao Geng, Ran Chen, Wanli Chen, Bei Yu
<span title="2019-12-16">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Recent researches have shown prominent advantages of machine learning techniques dealing with complicated and big data problems, which bring potential of dedicated machine learning solution for DFM problems  ...  Preliminary results show the efficiency and effectiveness of proposed frameworks that have the potential to be alternatives to existing EDA solutions.  ...  II Hotspot Detection via Machine Learning A Shallow Machine Learning Solutions Before the exploding of deep neural networks, traditional machine learning solutions have been deeply investigated to detect  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.07254v1">arXiv:1912.07254v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/x7gt5oobbvb3jhmujrgn6xmxju">fatcat:x7gt5oobbvb3jhmujrgn6xmxju</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200906005440/https://arxiv.org/pdf/1912.07254v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e8/8f/e88f1c6877b6a9e50afaf816a2aef0ea213c6bd7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.07254v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Machine learning and pattern matching in physical design

Bei Yu, David Z. Pan, Tetsuaki Matsunawa, Xuan Zeng
<span title="">2015</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fkjmyf3l45eo5ovjdnpeqpdjd4" style="color: black;">The 20th Asia and South Pacific Design Automation Conference</a> </i> &nbsp;
In this paper, we will discuss key techniques and recent results of machine learning and pattern matching, with their applications in physical design.  ...  in VLSI physical design (including physical verification), e.g., lithography hotspot detection and data/pattern-driven physical design, as ML and PM can raise the level of abstraction from detailed physics-based  ...  Lithography Hotspot Detection A.1 Machine Learning Approach In physical design and verification stages, the hotspot detection problem is to locate hotspots on a given layout with fast turnaround-time  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/aspdac.2015.7059020">doi:10.1109/aspdac.2015.7059020</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/aspdac/YuPMZ15.html">dblp:conf/aspdac/YuPMZ15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sx5v4nvh3fhwvfxqida6mo4ezq">fatcat:sx5v4nvh3fhwvfxqida6mo4ezq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20150319010328/http://www.cerc.utexas.edu:80/~bei/papers/C30_ASPDAC2015_MLPD.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d2/69/d2691c4d7e2a2e2061aa1d3455adebfff8db65a0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/aspdac.2015.7059020"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

AENEID

Duo Ding, Jhih-Rong Gao, Kun Yuan, David Z. Pan
<span title="">2011</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5vn6yyeefbbxtoo3uhwxwjwtme" style="color: black;">Proceedings of the 48th Design Automation Conference on - DAC &#39;11</a> </i> &nbsp;
AENEID combines novel hotspot detection and routing path prediction techniques through modern data learning methods and applies them at the detailed routing stage to drive high fidelity lithography-friendly  ...  Compared with existing litho-friendly routing works, AENEID demonstrates 26% to 66% (avg. 50%) of lithography hotspot reduction at the cost of only 18%-38% (avg. 30%) of run-time overhead.  ...  To further improve runtime and detection coverage, a hierarchically refined machine learning framework is proposed in [13] for fast speed high performance hotspot detection using both Artificial Neural  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2024724.2024902">doi:10.1145/2024724.2024902</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/dac/DingGYP11.html">dblp:conf/dac/DingGYP11</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6322px5oujaajnd3njikktelzm">fatcat:6322px5oujaajnd3njikktelzm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20111119094229/http://www.cerc.utexas.edu:80/~ding/paper/AENEID_DAC2011.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/21/29/2129160947f8e488600352178bbcd3df9a62a94e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2024724.2024902"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

EPIC: Efficient prediction of IC manufacturing hotspots with a unified meta-classification formulation

Duo Ding, Bei Yu, Joydeep Ghosh, David Z. Pan
<span title="">2012</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fkjmyf3l45eo5ovjdnpeqpdjd4" style="color: black;">17th Asia and South Pacific Design Automation Conference</a> </i> &nbsp;
EPIC proposes a unified framework to combine different hotspot detection methods together, such as machine learning and pattern matching, using mathematical programming/optimization.  ...  In this paper we present EPIC, an efficient and effective predictor for IC manufacturing hotspots in deep sub-wavelength lithography.  ...  In order to better address the problem, we propose EPIC : an efficient meta-classification formulation ( Fig. 1 ) to combine various hotspot detection techniques into a unified and automated framework  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/aspdac.2012.6164956">doi:10.1109/aspdac.2012.6164956</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/aspdac/DingYGP12.html">dblp:conf/aspdac/DingYGP12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/eomeveldwvaz7hkrxbptubpoom">fatcat:eomeveldwvaz7hkrxbptubpoom</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170922010214/http://www.ideal.ece.utexas.edu/pubs/pdf/2012/diyu12.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/89/3c/893c7f24252c51cfa1a91e2d7f16404f5a5647d5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/aspdac.2012.6164956"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Dealing with Aging and Yield in Scaled Technologies [chapter]

Wei Ye, Mohamed Baker Alawieh, Che-Lun Hsu, Yibo Lin, David Z. Pan
<span title="2020-12-10">2020</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jchr2ocdj5hfhk4vv3g3huukfi" style="color: black;">Embedded Systems</a> </i> &nbsp;
The presented techniques vary from analytical approaches to machine learning, and often require cross-layer information feedback for robust design cycles.  ...  Different fundamental effects such as device aging, interconnect electromigration, and process variations are investigated with the state-of-the-art techniques for modeling and optimization.  ...  Lithography Hotspot Detection with Machine Learning Models Various machine learning models have been used as hotspot detection kernels with the goal of achieving high accuracy and low false alarms, including  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-52017-5_17">doi:10.1007/978-3-030-52017-5_17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/shivkxo2afchtexgievin4hotq">fatcat:shivkxo2afchtexgievin4hotq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210429105419/https://link.springer.com/content/pdf/10.1007%2F978-3-030-52017-5_17.pdf?error=cookies_not_supported&amp;code=ca60bf2d-2d61-463c-bd4a-7be9f832a62b" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0c/28/0c28ce0f59bd83965c57f343aed96966db6ba559.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-52017-5_17"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a>

Machine learning for inverse lithography: using stochastic gradient descent for robust photomask synthesis

Ningning Jia, Edmund Y Lam
<span title="2010-03-31">2010</span> <i title="IOP Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ohjzwmpi25bm5hfuwnimdgk6fe" style="color: black;">Journal of Optics</a> </i> &nbsp;
The stochastic gradient descent approach, which is a useful tool in machine learning, is adopted to train the mask design.  ...  Inverse lithography technology (ILT) synthesizes photomasks by solving an inverse imaging problem through optimization of an appropriate functional.  ...  This approach has been used in optical lithography, for example, for hotspot detection [27] and variability prediction [28] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1088/2040-8978/12/4/045601">doi:10.1088/2040-8978/12/4/045601</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tbvzc7jcbjgcpioahh4kafklv4">fatcat:tbvzc7jcbjgcpioahh4kafklv4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170808130357/https://www.eee.hku.hk/optima/pub/journal/1004_JO.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a9/e6/a9e631fb0cbad5187906d9bae39c196a8b2e2b7b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1088/2040-8978/12/4/045601"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> iop.org </button> </a>

Design for Manufacturing With Emerging Nanolithography

David Z. Pan, Bei Yu, Jhih-Rong Gao
<span title="">2013</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rl7xk4fwazdrred2difr6v3lii" style="color: black;">IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems</a> </i> &nbsp;
In this paper, we survey key design for manufacturing issues for extreme scaling with emerging nanolithography technologies, including double/multiple patterning lithography, extreme ultraviolet lithography  ...  Recent results and examples will be discussed to show the enablement and effectiveness of such design and process integration, including lithography model/analysis, mask synthesis, and lithography friendly  ...  Acknowledgment The authors would like to thank Dr. L. Liebmann and Dr. R. Puri, IBM, for their helpful discussions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tcad.2013.2276751">doi:10.1109/tcad.2013.2276751</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/amxc565rjfg6bkliymbbbjczde">fatcat:amxc565rjfg6bkliymbbbjczde</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20150531211426/http://www.cerc.utexas.edu/utda/publications/J45.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/6e/54/6e541c7ff21fb329a99dbd37296b576cd25daeca.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tcad.2013.2276751"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

A Machine Learning Based Framework for Sub-Resolution Assist Feature Generation

Xiaoqing Xu, Tetsuaki Matsunawa, Shigeki Nojima, Chikaaki Kodama, Toshiya Kotani, David Z. Pan
<span title="">2016</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pboyhm4o35h5zf2v7usd2ygk5a" style="color: black;">Proceedings of the 2016 on International Symposium on Physical Design - ISPD &#39;16</a> </i> &nbsp;
This paper proposes the first machine learning based framework for fast yet consistent SRAF generation with high quality of results.  ...  Experimental results demonstrate that, compared with commercial Calibre tool, our machine learning based SRAF generation obtains 10X speed up and comparable performance in terms of edge placement error  ...  The authors would like to thank the memory lithography group (MLG) in Toshiba Corporation for the helpful discussions and feedback on this work.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2872334.2872357">doi:10.1145/2872334.2872357</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/ispd/XuMNKKP16.html">dblp:conf/ispd/XuMNKKP16</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ousswdkjwvedploj3pk5hvbddu">fatcat:ousswdkjwvedploj3pk5hvbddu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180328172920/http://www.cerc.utexas.edu:80/utda/publications/C191.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/cc/c5/ccc5271f160ee1178c3af4fe9d4bf23894dd4a91.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2872334.2872357"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Robust and resilient designs from the bottom-up: Technology, CAD, circuit, and system issues

Vijay Janapa Reddi, David Z. Pan, Sani R. Nassif, Keith A. Bowman
<span title="">2012</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fkjmyf3l45eo5ovjdnpeqpdjd4" style="color: black;">17th Asia and South Pacific Design Automation Conference</a> </i> &nbsp;
In this paper, we describe an interdisciplinary effort toward robust and resilient designs that mitigate the effects of device and circuit parameter variations in order to enhance system performance, energy  ...  Existing works for lithography hotspot characterization/identification fall into two major categories: pattern matching techniques [12] , [13] and machine learning/data mining [14] - [16] techniques  ...  On the other hand, machine learning based methods provide highly flexible models with low false alarms, but their accuracies still need to be improved.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/aspdac.2012.6165064">doi:10.1109/aspdac.2012.6165064</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/aspdac/ReddiPNB12.html">dblp:conf/aspdac/ReddiPNB12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/khckyzmudvc6xfx2lgclvhioke">fatcat:khckyzmudvc6xfx2lgclvhioke</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20150531213327/http://www.cerc.utexas.edu/utda/publications/C129.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4d/ef/4def0494bed392563751d81f16bde4603a085967.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/aspdac.2012.6165064"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Data Efficient Lithography Modeling with Transfer Learning and Active Data Selection [article]

Yibo Lin, Meng Li, Yuki Watanabe, Taiki Kimura, Tetsuaki Matsunawa, Shigeki Nojima, David Z. Pan
<span title="2018-06-27">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Our framework based on transfer learning and active learning techniques is effective within a competitive range of accuracy, i.e., 3-10X reduction on the amount of training data with comparable accuracy  ...  to the state-of-the-art learning approach.  ...  The authors would like to thank Memory Lithography Group from Toshiba Memory Corporation and Dr. Kai Zhong from the Computer Science Department of UT Austin for helpful discussions and feedback.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1807.03257v1">arXiv:1807.03257v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lrqfucozoraaxcgbjepnkruo7u">fatcat:lrqfucozoraaxcgbjepnkruo7u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191017064535/https://arxiv.org/pdf/1807.03257v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/37/d2/37d28a70b4f1ea798a6ec46440ad33d3a14eae53.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1807.03257v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Understanding Multidimensional Verification: Where Functional Meets Non-Functional

Xinhui Lai, Aneesh Balakrishnan, Thomas Lange, Maksim Jenihhin, Tara Ghasempouri, Jaan Raik, Dan Alexandrescu
<span title="">2019</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/brvj2ugukfgvhevdy5lwzvdy6m" style="color: black;">Microprocessors and microsystems</a> </i> &nbsp;
Further, an initial approach to perform multidimensional verification based on machine learning techniques is evaluated.  ...  In recent years, numerous extrafunctional aspects of electronic systems were brought to the front and imply verification of hardware design models in multidimensional space along with the functional concerns  ...  ., lithography hotspot detection) was investigated, and a reference model for application was presented.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.micpro.2019.102867">doi:10.1016/j.micpro.2019.102867</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fep3q5wlmnafdoh2jjlfheauwq">fatcat:fep3q5wlmnafdoh2jjlfheauwq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210429020008/https://www.openaccessrepository.it/record/37712/files/fulltext.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5f/05/5f058cc620ab2f008c84866a6042988a85d0e3b7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.micpro.2019.102867"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Physically-aware analysis of systematic defects in integrated circuits

Wing Chiu Tam, R. D. Blanton
<span title="">2011</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/c6t4qycpy5haxkpjvywsoox6dq" style="color: black;">2011 IEEE International Test Conference</a> </i> &nbsp;
Defects that arise due to certain aspects of the design being sensitive to the manufacturing process with an elevated likelihood of failure are known as systematic defects.  ...  These features pinpoint potential systematic defects, because, by definition, systematic defects are caused by hard-to-manufacture features with an increased likelihood of failure, which implies that systematic  ...  Finding a better and more efficient heuristic for identifying the optimal value of K is an open problem in the machine-learning community.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/test.2011.6139137">doi:10.1109/test.2011.6139137</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/itc/TamB11.html">dblp:conf/itc/TamB11</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tkopielnszghvnl6t46tagvipm">fatcat:tkopielnszghvnl6t46tagvipm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809095719/https://www.ece.cmu.edu/research/publications/2012/CMU-ECE-2012-013.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4b/1d/4b1d93ea6ce9f12e2775907e895365234a264dca.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/test.2011.6139137"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Physically-Aware Analysis of Systematic Defects in Integrated Circuits

Wing Chiu Tam, R. D. Blanton
<span title="">2012</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hkpx3vsnhrfb7jh6hlwads7olq" style="color: black;">IEEE Design &amp; Test of Computers</a> </i> &nbsp;
Defects that arise due to certain aspects of the design being sensitive to the manufacturing process with an elevated likelihood of failure are known as systematic defects.  ...  These features pinpoint potential systematic defects, because, by definition, systematic defects are caused by hard-to-manufacture features with an increased likelihood of failure, which implies that systematic  ...  Finding a better and more efficient heuristic for identifying the optimal value of K is an open problem in the machine-learning community.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/mdt.2012.2211093">doi:10.1109/mdt.2012.2211093</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gic5ouugzngmfgcbbdrnvebsh4">fatcat:gic5ouugzngmfgcbbdrnvebsh4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809095719/https://www.ece.cmu.edu/research/publications/2012/CMU-ECE-2012-013.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4b/1d/4b1d93ea6ce9f12e2775907e895365234a264dca.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/mdt.2012.2211093"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Analytics-statistics mixed training and its fitness to semisupervised manufacturing

Parag Parashar, Chun Han Chen, Chandni Akbar, Sze Ming Fu, Tejender S. Rawat, Sparsh Pratik, Rajat Butola, Shih Han Chen, Albert S. Lin, Jie Zhang
<span title="2019-08-13">2019</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
In this paper, we use an analytics-statistics mixed training (ASMT) approach using TCAD. Under this method, the TCAD models are incorporated into the machine learning training procedure.  ...  With the application of ASMT to the BOSCH process, we show that the mean square error (MSE) can be effectively decreased when the analytics-statistics mixed training (ASMT) scheme is used instead of the  ...  [27] used machine learning to detect lithography hotspots on wafers.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0220607">doi:10.1371/journal.pone.0220607</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31408473">pmid:31408473</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6692054/">pmcid:PMC6692054</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nby6oi4gnbf4hbqbeuzsubxwj4">fatcat:nby6oi4gnbf4hbqbeuzsubxwj4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191214051704/https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0220607&amp;type=printable" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/72/b3/72b395b0b5d0aab1c7b6d6dbcdfd7d7580bc431e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0220607"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6692054" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
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