Pub Date : 2025-02-25DOI: 10.1109/TSM.2025.3540179
{"title":"Call for Nominations for Editor-in-Chief: IEEE Transactions on Semiconductor Manufacturing","authors":"","doi":"10.1109/TSM.2025.3540179","DOIUrl":"https://doi.org/10.1109/TSM.2025.3540179","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"38 1","pages":"111-111"},"PeriodicalIF":2.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10903517","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489055","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 : 2025-02-25DOI: 10.1109/TSM.2025.3534595
{"title":"Call for Papers for Journal of Lightwave Technology: Special Issue on OFS-29","authors":"","doi":"10.1109/TSM.2025.3534595","DOIUrl":"https://doi.org/10.1109/TSM.2025.3534595","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"38 1","pages":"110-110"},"PeriodicalIF":2.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10903546","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489221","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 : 2025-02-25DOI: 10.1109/TSM.2025.3534591
{"title":"Call for Papers for a Special Issue of IEEE Transactions on Electron Devices on \"Wide Band Gap Semiconductors for Automotive Applications\"","authors":"","doi":"10.1109/TSM.2025.3534591","DOIUrl":"https://doi.org/10.1109/TSM.2025.3534591","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"38 1","pages":"106-107"},"PeriodicalIF":2.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10903515","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489052","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 : 2025-02-25DOI: 10.1109/TSM.2025.3534593
{"title":"Call for Papers for a Special Issue of IEEE Transactions on Materials for Electron Devices: \"Exploration of the Exciting World of Multifunctional Oxide-Based Electronic Devices: From Material to System-Level Applications\"","authors":"","doi":"10.1109/TSM.2025.3534593","DOIUrl":"https://doi.org/10.1109/TSM.2025.3534593","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"38 1","pages":"108-109"},"PeriodicalIF":2.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10903522","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489053","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 : 2025-02-25DOI: 10.1109/TSM.2025.3534606
{"title":"IEEE Transactions on Semiconductor Manufacturing Information for Authors","authors":"","doi":"10.1109/TSM.2025.3534606","DOIUrl":"https://doi.org/10.1109/TSM.2025.3534606","url":null,"abstract":"","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"38 1","pages":"C3-C3"},"PeriodicalIF":2.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10903151","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489051","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 : 2025-01-20DOI: 10.1109/TSM.2025.3528735
Taeyoung Kim;Shilong Zhang;Youngsoo Shin
Model-based optical proximity correction (MB- OPC) relies on a feedback loop, in which correction result, measured as edge placement error (EPE), is used for decision of next correction. A proportional-integral-derivative (PID) control is a popular mechanism employed for such feedback loop, but current MB-OPC usually relies only on P control. This is because there is no systematic way to customize P, I, and D coefficients for different layouts in different OPC iterations.We apply reinforcement learning (RL) to construct the trained actor that adaptively yields PID coefficients within the correction loop. The RL model consists of an actor and a critic. We perform supervised pre-training to quickly set the initial weights of RL model, with the actor mimicking standard MB-OPC. Subsequently, the critic is trained to predict accurate Q-value, the cumulative reward from OPC correction. The actor is then trained to maximize this Q-value. Experiments are performed with aggressive target maximum EPE values. The proposed OPC for test layouts requires 5 to 7 iterations, while standard MB-OPC (with constant coefficient-based control) completes in 20 to 28 iterations. This reduces OPC runtime to about 1/2.7 on average. In addition, maximum EPE is also reduced by about 24%.
{"title":"Model-Based OPC With Adaptive PID Control Through Reinforcement Learning","authors":"Taeyoung Kim;Shilong Zhang;Youngsoo Shin","doi":"10.1109/TSM.2025.3528735","DOIUrl":"https://doi.org/10.1109/TSM.2025.3528735","url":null,"abstract":"Model-based optical proximity correction (MB- OPC) relies on a feedback loop, in which correction result, measured as edge placement error (EPE), is used for decision of next correction. A proportional-integral-derivative (PID) control is a popular mechanism employed for such feedback loop, but current MB-OPC usually relies only on P control. This is because there is no systematic way to customize P, I, and D coefficients for different layouts in different OPC iterations.We apply reinforcement learning (RL) to construct the trained actor that adaptively yields PID coefficients within the correction loop. The RL model consists of an actor and a critic. We perform supervised pre-training to quickly set the initial weights of RL model, with the actor mimicking standard MB-OPC. Subsequently, the critic is trained to predict accurate Q-value, the cumulative reward from OPC correction. The actor is then trained to maximize this Q-value. Experiments are performed with aggressive target maximum EPE values. The proposed OPC for test layouts requires 5 to 7 iterations, while standard MB-OPC (with constant coefficient-based control) completes in 20 to 28 iterations. This reduces OPC runtime to about 1/2.7 on average. In addition, maximum EPE is also reduced by about 24%.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"38 1","pages":"48-56"},"PeriodicalIF":2.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489222","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 : 2025-01-17DOI: 10.1109/TSM.2025.3530964
Jingyu Park;Byeongsun Yoo;Song Yi Baek;Chulkyu Youn;Sundoo Kim;Dowan Kim;Sangho Roh;Se Jun Park;Jaehyun Kim;Changsoo Lee;Chulhwan Choi
This study focuses on the criticality of failure detection and condition-based maintenance (CBM) within the semiconductor industry, employing Fault Detection and Classification (FDC) systems and Machine Learning (ML) techniques for equipment log analysis to anticipate equipment conditions and timely maintenance. Initiatives emphasize the cultivation of data engineering experts, enhancing depth in data analytics and equipment monitoring. Moreover, the imperative to advance the field lies in the development of innovative sensor technologies, a task that necessitates close collaboration with equipment manufacturers. This strategic partnership is indispensable for augmenting the precision and breadth of data acquisition. It ultimately enables more sophisticated analytics, thereby facilitating the creation of advanced predictive failure models through enhanced data capture and analysis. This paper illustrates the semiconductor sector’s competitive adoption of diverse strategies and technologies for maintenance innovation, aiming to bolster industry productivity, equipment reliability, and sustainability. Such endeavors are pivotal for outlining the future trajectory of manufacturing and ensuring sustainable growth within the industry.
{"title":"Advancing Condition-Based Maintenance in the Semiconductor Industry: Innovations, Challenges and Future Directions for Predictive Maintenance","authors":"Jingyu Park;Byeongsun Yoo;Song Yi Baek;Chulkyu Youn;Sundoo Kim;Dowan Kim;Sangho Roh;Se Jun Park;Jaehyun Kim;Changsoo Lee;Chulhwan Choi","doi":"10.1109/TSM.2025.3530964","DOIUrl":"https://doi.org/10.1109/TSM.2025.3530964","url":null,"abstract":"This study focuses on the criticality of failure detection and condition-based maintenance (CBM) within the semiconductor industry, employing Fault Detection and Classification (FDC) systems and Machine Learning (ML) techniques for equipment log analysis to anticipate equipment conditions and timely maintenance. Initiatives emphasize the cultivation of data engineering experts, enhancing depth in data analytics and equipment monitoring. Moreover, the imperative to advance the field lies in the development of innovative sensor technologies, a task that necessitates close collaboration with equipment manufacturers. This strategic partnership is indispensable for augmenting the precision and breadth of data acquisition. It ultimately enables more sophisticated analytics, thereby facilitating the creation of advanced predictive failure models through enhanced data capture and analysis. This paper illustrates the semiconductor sector’s competitive adoption of diverse strategies and technologies for maintenance innovation, aiming to bolster industry productivity, equipment reliability, and sustainability. Such endeavors are pivotal for outlining the future trajectory of manufacturing and ensuring sustainable growth within the industry.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"38 1","pages":"96-105"},"PeriodicalIF":2.3,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489112","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 : 2025-01-17DOI: 10.1109/TSM.2025.3530971
Heehong Lee;Hyosung Lee;Dirk van Leuken;Hyungju Rah;Wout Keijers;Seunghui Seon;Younghwi Kim;Ijen van Mil;Yongchan Kim;Byungdeog Choi
Local critical dimension uniformity (LCDU) or line width roughness (LWR) is increasingly important in argon fluoride (ArF) immersion lithography systems (scanners) due to its growing contribution to edge placement error (EPE), an important parameter for circuit designers. A significant scanner contributor to LCDU is speckle, a light interference pattern that arises due to random coherent wavelet interference. In lithography systems (scanners), speckle will result in non-uniform dose delivery to the mask, causing local CD variations of the patterns imaged in the resist. This is an unwanted effect that potentially results in defects and should thus be controlled. In this work, existing lithographic scanner knobs are used to vary speckle contrast to showcase what product performance gain it can bring on LWR. This is achieved by slowing down the exposure speed, decreasing speckle contrast due to an increased number of pulses that fit in the exposure slit. However, this simultaneously brings scanner dynamics improvement that enhances imaging contrast which in turn also improves LWR. In order to decouple the dynamics and speckle improvement, additional experiments are required. This is done by restoring the speckle contrast for the slowed down exposures by adjusting the pulsed laser repetition rate. In the end, this series of experiments leads to a powerful framework to evaluate solely the speckle gain to product performance. In this work, the method is used to predict the LWR performance gain of the new ASML pulse stretcher which is designed to improve speckle contrast. Next to that, simulations are performed which accurately forecast the experimental results, demonstrating the robustness of the proposed framework. This work not only offers insights into optimizing the lithographic processes for improved product performance but also lays the groundwork for further exploration into scanner control strategies to minimize LWR and enhance yield in semiconductor manufacturing.
{"title":"Controlling Speckle Contrast Using Existing Lithographic Scanner Knobs to Explore the Impact on Line Width Roughness","authors":"Heehong Lee;Hyosung Lee;Dirk van Leuken;Hyungju Rah;Wout Keijers;Seunghui Seon;Younghwi Kim;Ijen van Mil;Yongchan Kim;Byungdeog Choi","doi":"10.1109/TSM.2025.3530971","DOIUrl":"https://doi.org/10.1109/TSM.2025.3530971","url":null,"abstract":"Local critical dimension uniformity (LCDU) or line width roughness (LWR) is increasingly important in argon fluoride (ArF) immersion lithography systems (scanners) due to its growing contribution to edge placement error (EPE), an important parameter for circuit designers. A significant scanner contributor to LCDU is speckle, a light interference pattern that arises due to random coherent wavelet interference. In lithography systems (scanners), speckle will result in non-uniform dose delivery to the mask, causing local CD variations of the patterns imaged in the resist. This is an unwanted effect that potentially results in defects and should thus be controlled. In this work, existing lithographic scanner knobs are used to vary speckle contrast to showcase what product performance gain it can bring on LWR. This is achieved by slowing down the exposure speed, decreasing speckle contrast due to an increased number of pulses that fit in the exposure slit. However, this simultaneously brings scanner dynamics improvement that enhances imaging contrast which in turn also improves LWR. In order to decouple the dynamics and speckle improvement, additional experiments are required. This is done by restoring the speckle contrast for the slowed down exposures by adjusting the pulsed laser repetition rate. In the end, this series of experiments leads to a powerful framework to evaluate solely the speckle gain to product performance. In this work, the method is used to predict the LWR performance gain of the new ASML pulse stretcher which is designed to improve speckle contrast. Next to that, simulations are performed which accurately forecast the experimental results, demonstrating the robustness of the proposed framework. This work not only offers insights into optimizing the lithographic processes for improved product performance but also lays the groundwork for further exploration into scanner control strategies to minimize LWR and enhance yield in semiconductor manufacturing.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"38 1","pages":"29-35"},"PeriodicalIF":2.3,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10844692","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489080","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 : 2025-01-09DOI: 10.1109/TSM.2025.3527514
Seohyun Kim;Shilong Zhang;Youngsoo Shin
In curvilinear optical proximity correction (OPC), each segment is modeled by a cubic Bézier curve, defined by two endpoints and two intermediate points. Iterative correction of these points is not trivial, and a simple heuristic (Chen et al., 2024) has been used but is not effective. A vertex placement error (VPE) is first introduced to replace edge placement error (EPE) in standard Manhattan OPC. Two machine learning models are applied for accurate curve correction. (1) An MLP is used to locate the new endpoints, while VPE from the previous iteration and a few PFT signals representing local light intensity are provided as inputs. (2) A VPE predictor, constructed with GCNs, is designed to output average (or maximum) VPE over a given layout clip. Once trained, it is used to identify intermediate points after new endpoints are fixed by MLP; this is done through gradient descent optimization such that VPE is minimized and curvature constraints are respected as much as possible. Experimental results demonstrate that the proposed curvilinear OPC reduces OPC iterations from 8 to 5 when average VPE is considered as a target or from 14 to 5 when maximum VPE is a target, with a final VPE reduction of about 5 to 6%.
{"title":"ML-Guided Curvilinear OPC: Fast, Accurate, and Manufacturable Curve Correction","authors":"Seohyun Kim;Shilong Zhang;Youngsoo Shin","doi":"10.1109/TSM.2025.3527514","DOIUrl":"https://doi.org/10.1109/TSM.2025.3527514","url":null,"abstract":"In curvilinear optical proximity correction (OPC), each segment is modeled by a cubic Bézier curve, defined by two endpoints and two intermediate points. Iterative correction of these points is not trivial, and a simple heuristic (Chen et al., 2024) has been used but is not effective. A vertex placement error (VPE) is first introduced to replace edge placement error (EPE) in standard Manhattan OPC. Two machine learning models are applied for accurate curve correction. (1) An MLP is used to locate the new endpoints, while VPE from the previous iteration and a few PFT signals representing local light intensity are provided as inputs. (2) A VPE predictor, constructed with GCNs, is designed to output average (or maximum) VPE over a given layout clip. Once trained, it is used to identify intermediate points after new endpoints are fixed by MLP; this is done through gradient descent optimization such that VPE is minimized and curvature constraints are respected as much as possible. Experimental results demonstrate that the proposed curvilinear OPC reduces OPC iterations from 8 to 5 when average VPE is considered as a target or from 14 to 5 when maximum VPE is a target, with a final VPE reduction of about 5 to 6%.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"38 1","pages":"19-28"},"PeriodicalIF":2.3,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489083","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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