微电子制造学报Pub Date : 2019-01-01DOI: 10.33079/jomm.19020401
M. Alawieh, Yibo Lin, Wei-Chen Ye, D. Pan
{"title":"Generative Learning in VLSI Design for Manufacturability: Current Status and Future Directions","authors":"M. Alawieh, Yibo Lin, Wei-Chen Ye, D. Pan","doi":"10.33079/jomm.19020401","DOIUrl":"https://doi.org/10.33079/jomm.19020401","url":null,"abstract":": With the continuous scaling of integrated circuit technologies, design for manufacturability (DFM) is becoming more critical, yet more challenging. Alongside, recent advances in machine learning have provided a new computing paradigm with promising applications in VLSI manufacturability. In particular, generative learning - regarded among the most interesting ideas in present-day machine learning - has demonstrated impressive capabilities in a wide range of applications. This paper surveys recent results of using generative learning in VLSI manufacturing modeling and optimization. Specifically, we examine the unique features of generative learning that have been leveraged to improve DFM efficiency in an unprecedented way; hence, paving the way to a new data-driven DFM approach. The state-of-the-art methods are presented, and challenges/opportunities are discussed.","PeriodicalId":66020,"journal":{"name":"微电子制造学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69492065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
微电子制造学报Pub Date : 2019-01-01DOI: 10.33079/JOMM.19020104
Huiling Zhu, P. Blackborow
{"title":"Laser-Driven Light Sources for Nanometrology Applications","authors":"Huiling Zhu, P. Blackborow","doi":"10.33079/JOMM.19020104","DOIUrl":"https://doi.org/10.33079/JOMM.19020104","url":null,"abstract":"","PeriodicalId":66020,"journal":{"name":"微电子制造学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69492312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Key Process Approach Recommendation for 5 nm Logic Process Flow with EUV Photolithography","authors":"Yushu Yang, Yanli Li, Qiang Wu, Jianjun Zhu, Shoumian Chen","doi":"10.33079/jomm.20030103","DOIUrl":"https://doi.org/10.33079/jomm.20030103","url":null,"abstract":"5 nm logic process is the current leading-edge technology which is under development in world-wide leading foundries. In a typical 5 nm logic process, the Fin pitch is 22~27 nm, the contact-poly pitch (CPP) is 48~55 nm, and the minimum metal pitch (MPP) is around 30~36 nm. Due to the fact that these pitches are much smaller than the resolution capability of 193 nm immersion lithography, it is also the first generation which adopts EUV photolithography technology on a large-scale where the process flow can be simplified by single exposure method from more than 10 layers. Relentless scaling brings big challenges to process integration and pushes each process module to the physical and material limit. Therefore, the success of process development will largely depend on careful balance the pros and cons to achieve both performance and yield targets. In the paper, we discussed the advantages and disadvantages of different process approaches for key process loops for 5 nm logic process flow, including dummy poly cut versus metal gate cut approaches in the metal gate loops, self-aligned contact (SAC) versus brutally aligned contact (BAC) approaches, and also introduced the self-aligned double patterning approach in the lower metal processes. Based on the above evaluation, we will provide a recommendation for module’s process development.","PeriodicalId":66020,"journal":{"name":"微电子制造学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69492549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Study of Inverse Lithography Approaches based on Deep Learning","authors":"Xianqiang Zhang, Xu Ma, Shengen Zhang, Yihua Pan, Gonzalo R Arce","doi":"10.33079/jomm.20030301","DOIUrl":"https://doi.org/10.33079/jomm.20030301","url":null,"abstract":": Computational lithography (CL) has become an indispensable technology to improve imaging resolution and fidelity of deep sub-wavelength lithography. The state-of-the-art CL approaches are capable of optimizing pixel-based mask patterns to effectively improve the degrees of optimization freedom. However, as the growth of data volume of photomask layouts, computational complexity has become a challenging problem that prohibits the applications of advanced CL algorithms. In the past, a number of innovative methods have been developed to improve the computational efficiency of CL algorithms, such as machine learning and deep learning methods. Based on the brief introduction of optical lithography, this paper reviews some recent advances of fast CL approaches based on deep learning. At the end, this paper briefly discusses some potential developments in future work.","PeriodicalId":66020,"journal":{"name":"微电子制造学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69492563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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