Pub Date : 2024-02-05DOI: 10.1109/TSM.2024.3359520
{"title":"Call for Papers for IEEE Transactions on Materials for Electron Devices","authors":"","doi":"10.1109/TSM.2024.3359520","DOIUrl":"https://doi.org/10.1109/TSM.2024.3359520","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 1","pages":"138-138"},"PeriodicalIF":2.7,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10419869","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-05DOI: 10.1109/TSM.2024.3356972
{"title":"Joint Call for Papers for IEEE Transactions on Semiconductor Manufacturing and IEEE Transactions on Electron Devices: Special Issue on Semiconductor Design for Manufacturing (DFM)","authors":"","doi":"10.1109/TSM.2024.3356972","DOIUrl":"https://doi.org/10.1109/TSM.2024.3356972","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 1","pages":"137-137"},"PeriodicalIF":2.7,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10419386","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-27DOI: 10.1109/TSM.2023.3347606
Ajay Kumar Dwivedi;Satyabrata Jit;Shweta Tripathi
This letter reports a SnS2 and ZnO nanocomposite (NC) prepared by dispersion method. The nanocomposite shows promising characteristics for optoelectronic application. SnS2:ZnO NC shows a wide absorption spectrum covering ultraviolet (UV)-visible-near infrared (NIR) regions. Hence, using the proposed nanocomposite a broadband photodetector with a structure comprising Al/ SnS2:ZnO/PEDOT:PSS/ Indium Tin Oxide (ITO) is fabricated. At a bias voltage of 1 V, the measured responsivity values (A/W) of the proposed device are 140.41, 848.63, and 1094.48 at 350 nm (UV), 750 nm (visible) and 900 nm (NIR), respectively.
{"title":"SnS₂ and ZnO Nanocomposite Prepared by Dispersion Method for Photodetector Application","authors":"Ajay Kumar Dwivedi;Satyabrata Jit;Shweta Tripathi","doi":"10.1109/TSM.2023.3347606","DOIUrl":"https://doi.org/10.1109/TSM.2023.3347606","url":null,"abstract":"This letter reports a SnS2 and ZnO nanocomposite (NC) prepared by dispersion method. The nanocomposite shows promising characteristics for optoelectronic application. SnS2:ZnO NC shows a wide absorption spectrum covering ultraviolet (UV)-visible-near infrared (NIR) regions. Hence, using the proposed nanocomposite a broadband photodetector with a structure comprising Al/ SnS2:ZnO/PEDOT:PSS/ Indium Tin Oxide (ITO) is fabricated. At a bias voltage of 1 V, the measured responsivity values (A/W) of the proposed device are 140.41, 848.63, and 1094.48 at 350 nm (UV), 750 nm (visible) and 900 nm (NIR), respectively.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 1","pages":"129-136"},"PeriodicalIF":2.7,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-15DOI: 10.1109/TSM.2023.3343633
Andy Ham;Myoung-Ju Park;John Fowler
This paper studies simultaneous scheduling of production and material transfer that arises in the semiconductor photolithography area. In particular, the right reticle and right job both need to be present to process the job. Jobs are transferred by a material handling system that employees a fleet of vehicles. Reticles serving as an auxiliary resource are also transferred from one place to another by a different set of vehicles. This intricate scheduling challenge, encompassing jobs, reticles, machines, and two distinct sets of vehicles, is explored here for the first time. The paper introduces a multi-stage methodology that involves relaxation, a constructive heuristic, constraint programming, and a warm-start approach to address this complex problem.
{"title":"Integrated Scheduling of Jobs, Tools, Machines, and Two Different Set of Transbots","authors":"Andy Ham;Myoung-Ju Park;John Fowler","doi":"10.1109/TSM.2023.3343633","DOIUrl":"https://doi.org/10.1109/TSM.2023.3343633","url":null,"abstract":"This paper studies simultaneous scheduling of production and material transfer that arises in the semiconductor photolithography area. In particular, the right reticle and right job both need to be present to process the job. Jobs are transferred by a material handling system that employees a fleet of vehicles. Reticles serving as an auxiliary resource are also transferred from one place to another by a different set of vehicles. This intricate scheduling challenge, encompassing jobs, reticles, machines, and two distinct sets of vehicles, is explored here for the first time. The paper introduces a multi-stage methodology that involves relaxation, a constructive heuristic, constraint programming, and a warm-start approach to address this complex problem.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 1","pages":"27-37"},"PeriodicalIF":2.7,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139694969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a linear regression model for scalar-valued responses and two-way functional (bivariate) predictors. Our motivation stems from the quality evaluation of products based on optical emission spectroscopy data from virtual metrology of semiconductor manufacturing. We focus on multivariate cases where the smoothness and shapes of the data vary significantly across variables. We propose a two-step solution to this problem, consisting of decomposition and prediction. First, we decompose the two-way functional data into pairs of component functions using functional singular value decomposition. Next, we build functional linear models for the decomposed functional variables and obtain the final predictor by averaging the models. Results from numerical studies, including simulation studies and real data analysis, demonstrate the promising empirical properties of the proposed approach, especially when the number of predictors is large.
{"title":"A Model Averaging Prediction of Two-Way Functional Data in Semiconductor Manufacturing","authors":"Soobin Kim;Youngwook Kwon;Joonpyo Kim;Kiwook Bae;Hee-Seok Oh","doi":"10.1109/TSM.2023.3339731","DOIUrl":"https://doi.org/10.1109/TSM.2023.3339731","url":null,"abstract":"This paper proposes a linear regression model for scalar-valued responses and two-way functional (bivariate) predictors. Our motivation stems from the quality evaluation of products based on optical emission spectroscopy data from virtual metrology of semiconductor manufacturing. We focus on multivariate cases where the smoothness and shapes of the data vary significantly across variables. We propose a two-step solution to this problem, consisting of decomposition and prediction. First, we decompose the two-way functional data into pairs of component functions using functional singular value decomposition. Next, we build functional linear models for the decomposed functional variables and obtain the final predictor by averaging the models. Results from numerical studies, including simulation studies and real data analysis, demonstrate the promising empirical properties of the proposed approach, especially when the number of predictors is large.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 1","pages":"76-86"},"PeriodicalIF":2.7,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139694892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-06DOI: 10.1109/TSM.2023.3340110
Mingyang Xia;Yan Yan;Chen Li;Xuelong Shi
To ensure post OPC data quality, examination based on estimated resist contours at resist bottom alone is insufficient, reliable prediction of lithography performance within process window must rely on complete information of on-wafer resist 3D structures. In this regard, resist 3D structure model, in particular, the through focus resist 3D structure model, with full chip capability will be the ultimate model in demand. To develop machine learning resist 3D structure models,we have proposed the physics-based information encoding scheme, together with carefully chosen deep convolution neural network and model training strategies. Our proposed through focus resist 3D structure model is based on conditional U-net structure with first five eigen images as model’s main inputs and the focus setting as the conditional input. The average normalized cross correlation (NCC) or mean structure similarity index between ground truth and model predicted resist 3D structures can reach 0.92. With single GPU (Tesla M60), it takes 6.1ms for the model to produce resist 3D structure covering area of 1.8umx1.8 $mu {mathrm{ m}}$ . The model is fast enough and can be engineered for full chip implementation. The model can extend the capability of detecting lithography process window aware resist loss related hotspots.
{"title":"A Unified Machine Learning Through Focus Resist 3-D Structure Model","authors":"Mingyang Xia;Yan Yan;Chen Li;Xuelong Shi","doi":"10.1109/TSM.2023.3340110","DOIUrl":"https://doi.org/10.1109/TSM.2023.3340110","url":null,"abstract":"To ensure post OPC data quality, examination based on estimated resist contours at resist bottom alone is insufficient, reliable prediction of lithography performance within process window must rely on complete information of on-wafer resist 3D structures. In this regard, resist 3D structure model, in particular, the through focus resist 3D structure model, with full chip capability will be the ultimate model in demand. To develop machine learning resist 3D structure models,we have proposed the physics-based information encoding scheme, together with carefully chosen deep convolution neural network and model training strategies. Our proposed through focus resist 3D structure model is based on conditional U-net structure with first five eigen images as model’s main inputs and the focus setting as the conditional input. The average normalized cross correlation (NCC) or mean structure similarity index between ground truth and model predicted resist 3D structures can reach 0.92. With single GPU (Tesla M60), it takes 6.1ms for the model to produce resist 3D structure covering area of 1.8umx1.8 \u0000<inline-formula> <tex-math>$mu {mathrm{ m}}$ </tex-math></inline-formula>\u0000. The model is fast enough and can be engineered for full chip implementation. The model can extend the capability of detecting lithography process window aware resist loss related hotspots.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 1","pages":"59-66"},"PeriodicalIF":2.7,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-05DOI: 10.1109/TSM.2023.3339330
Thomas J. Ashby;Vincent Truffert;Dorin Cerbu;Kit Ausschnitt;Anne-Laure Charley;Wilfried Verachtert;Roel Wuyts
Doing high throughput high accuracy metrology in small geometries is challenging. One approach is to build easily measurable proxy targets onto dies and make a predictive model based on those signals. We use optical Pattern Shift Response (PSR) proxy targets to build predictive models of the electrical characteristics of devices in the Back End Of Line (BEOL). Given the wide choice of PSR targets, we explore how to select combinations of them to maximise the utility of the features for building an accurate Machine Learning (ML) model; we call this approach Multiplexed Optical Metrology. We also explore the trade-off between chip area dedicated to targets and achievable accuracy. We run ML experiments using different selections of targets measured at different stages of BEOL processing: post-lithography and post-Chemical-Mechanical-Planarisation (CMP). Our results show that a) reasonable predictive performance can be achieved for a reasonable area budget; b) ML model performance across target families varies significantly, thus justifying the need for careful selection of targets; c) longitudinal measurements of targets increases accuracy for no extra area penalty; d) increasing the number of targets gives some improvement in accuracy for a dataset of this size, but relatively small compared to the increase in area budget needed. Ultimately we aim to do die-level yield prediction using these techniques. We discuss how collecting a larger dataset with appropriate yield information is the logical next step to achieving this.
在小型几何结构中进行高通量、高精度测量具有挑战性。一种方法是在模具上建立易于测量的替代目标，并根据这些信号建立预测模型。我们使用光学模式偏移响应（PSR）代理目标来建立生产线后端（BEOL）器件电气特性的预测模型。鉴于有多种 PSR 目标可供选择，我们探讨了如何选择这些目标的组合，以最大限度地利用这些特征来建立精确的机器学习 (ML) 模型；我们将这种方法称为多路复用光学计量学。我们还探索了专用于目标的芯片面积与可实现的精度之间的权衡。我们使用在 BEOL 处理的不同阶段（光刻后和化学机械平坦化 (CMP) 后）测量的不同目标选择进行 ML 实验。我们的结果表明：a) 在合理的面积预算下，可以实现合理的预测性能；b) 不同目标系列的 ML 模型性能差异很大，因此需要仔细选择目标；c) 纵向测量目标可以提高准确性，而不会增加额外的面积损失；d) 增加目标数量可以在一定程度上提高这种规模数据集的准确性，但与所需增加的面积预算相比，提高的幅度相对较小。我们的最终目标是利用这些技术进行芯片级良率预测。我们将讨论收集具有适当良率信息的更大数据集如何成为实现这一目标的合理的下一步。
{"title":"Machine Learning on Multiplexed Optical Metrology Pattern Shift Response Targets to Predict Electrical Properties","authors":"Thomas J. Ashby;Vincent Truffert;Dorin Cerbu;Kit Ausschnitt;Anne-Laure Charley;Wilfried Verachtert;Roel Wuyts","doi":"10.1109/TSM.2023.3339330","DOIUrl":"https://doi.org/10.1109/TSM.2023.3339330","url":null,"abstract":"Doing high throughput high accuracy metrology in small geometries is challenging. One approach is to build easily measurable proxy targets onto dies and make a predictive model based on those signals. We use optical Pattern Shift Response (PSR) proxy targets to build predictive models of the electrical characteristics of devices in the Back End Of Line (BEOL). Given the wide choice of PSR targets, we explore how to select combinations of them to maximise the utility of the features for building an accurate Machine Learning (ML) model; we call this approach Multiplexed Optical Metrology. We also explore the trade-off between chip area dedicated to targets and achievable accuracy. We run ML experiments using different selections of targets measured at different stages of BEOL processing: post-lithography and post-Chemical-Mechanical-Planarisation (CMP). Our results show that a) reasonable predictive performance can be achieved for a reasonable area budget; b) ML model performance across target families varies significantly, thus justifying the need for careful selection of targets; c) longitudinal measurements of targets increases accuracy for no extra area penalty; d) increasing the number of targets gives some improvement in accuracy for a dataset of this size, but relatively small compared to the increase in area budget needed. Ultimately we aim to do die-level yield prediction using these techniques. We discuss how collecting a larger dataset with appropriate yield information is the logical next step to achieving this.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 1","pages":"46-58"},"PeriodicalIF":2.7,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10342754","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-29DOI: 10.1109/TSM.2023.3337720
Jila Rafighdoost;Dmytro Kolenov;Silvania F. Pereira
It has been a widely growing interest in using silicon carbide (SiC) in high-power electronic devices. Yet, SiC wafers may contain killer defects that could reduce fabrication yield and make the device fall into unexpected failures. To prevent these failures from happening, it is very important to develop inspection tools that can detect, characterize and locate these defects in a non-invasive way. Current inspection techniques such as Dark Field or Bright field microscopy are effectively able to visualize most such defects; however, there are some scenarios where the inspection becomes problematic or almost impossible, such as when the defects are too small or have low contrast or if the defects lie deep into the substrate. Thus, an alternative method is needed to face these challenges. In this paper, we demonstrate the application of coherent Fourier scatterometry (CFS) as a complementary tool in addition to the conventional techniques to overcome different and problematic scenarios of killer defects inspection on SiC samples. Scanning electron microscopy (SEM) has been used to assess the same defects to validate the findings of CFS. Great consistency has been demonstrated in the comparison between the results obtained with CFS and SEM.
人们对在大功率电子设备中使用碳化硅（SiC）的兴趣与日俱增。然而，碳化硅晶片可能含有致命缺陷，这些缺陷可能会降低制造良率，使设备出现意外故障。为了防止这些故障的发生，开发能够以非侵入方式检测、表征和定位这些缺陷的检测工具非常重要。目前的检测技术（如暗视野或明视野显微镜）能够有效地观察到大多数此类缺陷；但在某些情况下，检测会出现问题或几乎不可能，例如当缺陷太小或对比度较低时，或者当缺陷位于基底深处时。因此，我们需要一种替代方法来应对这些挑战。在本文中，我们展示了相干傅立叶散射测量法（CFS）作为传统技术之外的一种补充工具，在检测 SiC 样品上的杀手级缺陷时如何克服不同的难题。扫描电子显微镜 (SEM) 被用来评估相同的缺陷，以验证 CFS 的发现。通过比较 CFS 和扫描电子显微镜获得的结果，发现两者具有很高的一致性。
{"title":"Coherent Fourier Scatterometry for Detection of Killer Defects on Silicon Carbide Samples","authors":"Jila Rafighdoost;Dmytro Kolenov;Silvania F. Pereira","doi":"10.1109/TSM.2023.3337720","DOIUrl":"10.1109/TSM.2023.3337720","url":null,"abstract":"It has been a widely growing interest in using silicon carbide (SiC) in high-power electronic devices. Yet, SiC wafers may contain killer defects that could reduce fabrication yield and make the device fall into unexpected failures. To prevent these failures from happening, it is very important to develop inspection tools that can detect, characterize and locate these defects in a non-invasive way. Current inspection techniques such as Dark Field or Bright field microscopy are effectively able to visualize most such defects; however, there are some scenarios where the inspection becomes problematic or almost impossible, such as when the defects are too small or have low contrast or if the defects lie deep into the substrate. Thus, an alternative method is needed to face these challenges. In this paper, we demonstrate the application of coherent Fourier scatterometry (CFS) as a complementary tool in addition to the conventional techniques to overcome different and problematic scenarios of killer defects inspection on SiC samples. Scanning electron microscopy (SEM) has been used to assess the same defects to validate the findings of CFS. Great consistency has been demonstrated in the comparison between the results obtained with CFS and SEM.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 1","pages":"124-128"},"PeriodicalIF":2.7,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10335636","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139256281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-28DOI: 10.1109/TSM.2023.3336169
Eric Herrmann;Xi Wang
The patterning of silicon and silicon oxide nanocones onto the surfaces of devices introduces interesting phenomena such as anti-reflection and super-transmissivity. While silicon nanocone formation is well-documented, current techniques to fabricate silicon oxide nanocones either involve complex fabrication procedures, non-deterministic placement, or poor uniformity. Here, we introduce a single-mask dry etching procedure for the fabrication of sharp silicon oxide nanocones with smooth sidewalls and deterministic distribution using electron beam lithography. Silicon oxide films deposited using plasma-enhanced chemical vapor deposition are etched using a thin alumina hard mask of selectivity > 88, enabling high aspect ratio nanocones with smooth sidewalls and arbitrary distribution across the target substrate. We further introduce a novel multi-step dry etching technique to achieve ultra-sharp amorphous silicon oxide nanocones with tip diameters of ~10 nm. The processes presented in this work may have applications in the fabrication of amorphous nanocone arrays onto arbitrary substrates or as nanoscale probes.
{"title":"Single-Mask Fabrication of Sharp SiOx Nanocones","authors":"Eric Herrmann;Xi Wang","doi":"10.1109/TSM.2023.3336169","DOIUrl":"https://doi.org/10.1109/TSM.2023.3336169","url":null,"abstract":"The patterning of silicon and silicon oxide nanocones onto the surfaces of devices introduces interesting phenomena such as anti-reflection and super-transmissivity. While silicon nanocone formation is well-documented, current techniques to fabricate silicon oxide nanocones either involve complex fabrication procedures, non-deterministic placement, or poor uniformity. Here, we introduce a single-mask dry etching procedure for the fabrication of sharp silicon oxide nanocones with smooth sidewalls and deterministic distribution using electron beam lithography. Silicon oxide films deposited using plasma-enhanced chemical vapor deposition are etched using a thin alumina hard mask of selectivity > 88, enabling high aspect ratio nanocones with smooth sidewalls and arbitrary distribution across the target substrate. We further introduce a novel multi-step dry etching technique to achieve ultra-sharp amorphous silicon oxide nanocones with tip diameters of ~10 nm. The processes presented in this work may have applications in the fabrication of amorphous nanocone arrays onto arbitrary substrates or as nanoscale probes.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 1","pages":"87-92"},"PeriodicalIF":2.7,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139694968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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