Pub Date : 2022-05-02DOI: 10.1109/asmc54647.2022.9792522
C. Garza, Cole Mann, Min Yuan, James B. Mattzela, Nik Snyder
Scrubbers play an essential role in the safe manufacturing of semiconductor devices. However, metal parts degrade very quickly in the highly corrosive scrubber environment, and this poses an environmental and safety challenge. In this paper, we present data for specialty coatings that significantly reduce the corrosion rate of metal scrubber parts. The benefits are a reduction of heavy metals in the waste stream, a minimized exposure of employees to the corroded parts, a solid-waste reduction, and a lower cost in part replacements. In short, the quality of the semiconductor process is improved, and the cost reduced.
{"title":"Specialty Coatings to Reduce Corrosion in Scrubber Components : Advanced Equipment Process and Materials","authors":"C. Garza, Cole Mann, Min Yuan, James B. Mattzela, Nik Snyder","doi":"10.1109/asmc54647.2022.9792522","DOIUrl":"https://doi.org/10.1109/asmc54647.2022.9792522","url":null,"abstract":"Scrubbers play an essential role in the safe manufacturing of semiconductor devices. However, metal parts degrade very quickly in the highly corrosive scrubber environment, and this poses an environmental and safety challenge. In this paper, we present data for specialty coatings that significantly reduce the corrosion rate of metal scrubber parts. The benefits are a reduction of heavy metals in the waste stream, a minimized exposure of employees to the corroded parts, a solid-waste reduction, and a lower cost in part replacements. In short, the quality of the semiconductor process is improved, and the cost reduced.","PeriodicalId":436890,"journal":{"name":"2022 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126629772","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}
Pub Date : 2022-05-02DOI: 10.1109/asmc54647.2022.9792526
P. Lenhard, Alexander Kovalenko, Radomír Lenhard
Given the integrated circuits (IC) production scale, the amount of process control monitoring (PCM) data enable to develop an efficient algorithm for IC yield prediction at the die-level. Therefore, in addition to cost-effective and time-efficient yield evaluation, the proposed model is able to identify failed dice and low-yield areas on a wafer without any direct electrical die testing. Additionally, for non-parametric random dice failure detection that are untraceable by PCM input based models, an ensemble learning including both PCM and die defect inspection data are described. As Wafer Sort (WS) consumes a lot of time and resources with high associated cost a significant cost reduction can be achieved using smart product routing with selective WS by employing the aforementioned die level predictive model.
{"title":"Integrated Circuit Die Level Yield Prediction Using Deep Learning","authors":"P. Lenhard, Alexander Kovalenko, Radomír Lenhard","doi":"10.1109/asmc54647.2022.9792526","DOIUrl":"https://doi.org/10.1109/asmc54647.2022.9792526","url":null,"abstract":"Given the integrated circuits (IC) production scale, the amount of process control monitoring (PCM) data enable to develop an efficient algorithm for IC yield prediction at the die-level. Therefore, in addition to cost-effective and time-efficient yield evaluation, the proposed model is able to identify failed dice and low-yield areas on a wafer without any direct electrical die testing. Additionally, for non-parametric random dice failure detection that are untraceable by PCM input based models, an ensemble learning including both PCM and die defect inspection data are described. As Wafer Sort (WS) consumes a lot of time and resources with high associated cost a significant cost reduction can be achieved using smart product routing with selective WS by employing the aforementioned die level predictive model.","PeriodicalId":436890,"journal":{"name":"2022 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128109387","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}
Pub Date : 2022-05-02DOI: 10.1109/asmc54647.2022.9792488
B. Anthouard, Valeria Borodin, Quentin Christ, S. Dauzére-Pérés, Renaud Roussel
Semiconductor manufacturing processes include more and more (queue) time constraints, often spanning multiple operations, which impact both production efficiency and quality. This paper presents a discrete-event simulation-based approach to support operators who manage lots under time constraints in a high-mix manufacturing environment. After motivating and stating the problem of managing time constraints, the main characteristics and capabilities of the proposed approach are presented. The approach is then validated with respect to the ground truth. Computational experiments conducted on industrial instances are discussed before providing conclusions and perspectives.
{"title":"A Simulation-Based Approach for Operational Management of Time Constraint Tunnels in Semiconductor Manufacturing : *Topic: IE: Industrial Engineering","authors":"B. Anthouard, Valeria Borodin, Quentin Christ, S. Dauzére-Pérés, Renaud Roussel","doi":"10.1109/asmc54647.2022.9792488","DOIUrl":"https://doi.org/10.1109/asmc54647.2022.9792488","url":null,"abstract":"Semiconductor manufacturing processes include more and more (queue) time constraints, often spanning multiple operations, which impact both production efficiency and quality. This paper presents a discrete-event simulation-based approach to support operators who manage lots under time constraints in a high-mix manufacturing environment. After motivating and stating the problem of managing time constraints, the main characteristics and capabilities of the proposed approach are presented. The approach is then validated with respect to the ground truth. Computational experiments conducted on industrial instances are discussed before providing conclusions and perspectives.","PeriodicalId":436890,"journal":{"name":"2022 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121605111","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}
Pub Date : 2022-05-02DOI: 10.1109/asmc54647.2022.9792477
J. Jeong, Taekyung Ha, Hyojeong Ji, S. J. Yoon
In semiconductor manufacturing, vacuum leakage occurring during wafer processing decreases productivity. Conventionally, the equipment is stopped to detect the vacuum leaks. However, this adversely affects productivity. We presents a real-time vacuum leak detection technique. We successfully identified vacuum leakage in real time by interpolating from the dry strip process parameters.
{"title":"Real-time vacuum leak detection technology to calculate vacuum leak parameters for dry stripping : EO: Equipment Optimization","authors":"J. Jeong, Taekyung Ha, Hyojeong Ji, S. J. Yoon","doi":"10.1109/asmc54647.2022.9792477","DOIUrl":"https://doi.org/10.1109/asmc54647.2022.9792477","url":null,"abstract":"In semiconductor manufacturing, vacuum leakage occurring during wafer processing decreases productivity. Conventionally, the equipment is stopped to detect the vacuum leaks. However, this adversely affects productivity. We presents a real-time vacuum leak detection technique. We successfully identified vacuum leakage in real time by interpolating from the dry strip process parameters.","PeriodicalId":436890,"journal":{"name":"2022 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130710397","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}
Pub Date : 2022-05-02DOI: 10.1109/asmc54647.2022.9792486
Hairong Lei, Qian Dong, C. Teh, Lingling Pu, C. Jen, Steve Lin
The proposed paper presents a case study describing how e-beam defect classification nuisance rate (NR) can be improved by the implementation of a new machine learning classification process in HMI e-Manager even for difficult data (feature boundary is overlay). This is important because low nuisance rate is an importance metric to measure the e-beam defect classification performance and it is usually difficult to obtain the low nuisance rate, especially for difficult defect dataset. Our machine learning (not a deep learning) multiple-phase classification results show that it is an effective way to improve the E-beam defect classification nuisance rate.
{"title":"Nuisance Rate Improvement of E-beam Defect Classification","authors":"Hairong Lei, Qian Dong, C. Teh, Lingling Pu, C. Jen, Steve Lin","doi":"10.1109/asmc54647.2022.9792486","DOIUrl":"https://doi.org/10.1109/asmc54647.2022.9792486","url":null,"abstract":"The proposed paper presents a case study describing how e-beam defect classification nuisance rate (NR) can be improved by the implementation of a new machine learning classification process in HMI e-Manager even for difficult data (feature boundary is overlay). This is important because low nuisance rate is an importance metric to measure the e-beam defect classification performance and it is usually difficult to obtain the low nuisance rate, especially for difficult defect dataset. Our machine learning (not a deep learning) multiple-phase classification results show that it is an effective way to improve the E-beam defect classification nuisance rate.","PeriodicalId":436890,"journal":{"name":"2022 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"320 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115833943","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}
Pub Date : 2022-05-02DOI: 10.1109/asmc54647.2022.9792478
V. Chan, A. Gasasira, R. Pujari, R. Southwick, I. Ok, S. Choi, C. Silvestre, G. Burr, N. Saulnier, S. Teehan, I. Ahsan
We discuss inline electrical testing to monitor the baseline of Analog Computing hardware using Phase Change Memory (PCM) technology. Tightening the PCM resistance distribution is necessary to meet analog computation requirement. A new yield methodology is introduced.
{"title":"Yield Methodology and Learning in Phase Change Memory (PCM) technology for Analog Computing : Topic/category: YE: Yield Enhancement/Learning, YM: Yield Methodologies","authors":"V. Chan, A. Gasasira, R. Pujari, R. Southwick, I. Ok, S. Choi, C. Silvestre, G. Burr, N. Saulnier, S. Teehan, I. Ahsan","doi":"10.1109/asmc54647.2022.9792478","DOIUrl":"https://doi.org/10.1109/asmc54647.2022.9792478","url":null,"abstract":"We discuss inline electrical testing to monitor the baseline of Analog Computing hardware using Phase Change Memory (PCM) technology. Tightening the PCM resistance distribution is necessary to meet analog computation requirement. A new yield methodology is introduced.","PeriodicalId":436890,"journal":{"name":"2022 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123651185","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}
Pub Date : 2022-05-02DOI: 10.1109/asmc54647.2022.9792502
Yu-Peng Cai
Filtration is critical to semiconductor processing applications such as photolithography and wafer cleaning, while filtration mechanisms can be especially complex due to the variety of solvents being used and complicated particle-filter interactions. In this paper, an imaging method enabled by confocal fluorescence microscopy is presented to directly reveal particle capture profiles in filtration membranes. Specifically, several filter membranes used for semiconductor processing applications have been studied, including polytetrafluoroethylene (PTFE), polyethylene (PE), and polyarylsulfone (PAS). This method can provide insights into filtration mechanisms and facilitate the design and optimization of filter membranes used for semiconductor processing applications.
{"title":"Visualization of Particle Retention Profiles in Advanced Filtration Media with Confocal Fluorescence Microscopy for Semiconductor Applications","authors":"Yu-Peng Cai","doi":"10.1109/asmc54647.2022.9792502","DOIUrl":"https://doi.org/10.1109/asmc54647.2022.9792502","url":null,"abstract":"Filtration is critical to semiconductor processing applications such as photolithography and wafer cleaning, while filtration mechanisms can be especially complex due to the variety of solvents being used and complicated particle-filter interactions. In this paper, an imaging method enabled by confocal fluorescence microscopy is presented to directly reveal particle capture profiles in filtration membranes. Specifically, several filter membranes used for semiconductor processing applications have been studied, including polytetrafluoroethylene (PTFE), polyethylene (PE), and polyarylsulfone (PAS). This method can provide insights into filtration mechanisms and facilitate the design and optimization of filter membranes used for semiconductor processing applications.","PeriodicalId":436890,"journal":{"name":"2022 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128546521","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}
Pub Date : 2022-05-02DOI: 10.1109/asmc54647.2022.9792505
Gregory M. Johnson, Thomas Rodgers, H. Stegmann, F. Hitzel
Measuring surface conduction points is a well-established analytical technique in SRAM failure analysis. A novel workflow and system have been developed that makes use of an Atomic Force Microscope (AFM) inside a Scanning Electron Microscope (SEM) and is capable of using standard laser deflection based probe tips. New results are provided on an 8T SRAM cell in 7 nm technology which demonstrate the ability to measure nFET, pFET, and gate contacts simultaneously with one scan, and with a topography measurement. A second analysis was performed to demonstrate the ability of the electron beam, combined with use of the AFM diamond tip as a scalpel, to expose subsurface layers and greatly improve current data. Furthermore, the system being in vacuum provides additional benefits in eliminating confounding effects.
{"title":"Conductive AFM in SEM for 7 nm and beyond : AM: Advanced Metrology","authors":"Gregory M. Johnson, Thomas Rodgers, H. Stegmann, F. Hitzel","doi":"10.1109/asmc54647.2022.9792505","DOIUrl":"https://doi.org/10.1109/asmc54647.2022.9792505","url":null,"abstract":"Measuring surface conduction points is a well-established analytical technique in SRAM failure analysis. A novel workflow and system have been developed that makes use of an Atomic Force Microscope (AFM) inside a Scanning Electron Microscope (SEM) and is capable of using standard laser deflection based probe tips. New results are provided on an 8T SRAM cell in 7 nm technology which demonstrate the ability to measure nFET, pFET, and gate contacts simultaneously with one scan, and with a topography measurement. A second analysis was performed to demonstrate the ability of the electron beam, combined with use of the AFM diamond tip as a scalpel, to expose subsurface layers and greatly improve current data. Furthermore, the system being in vacuum provides additional benefits in eliminating confounding effects.","PeriodicalId":436890,"journal":{"name":"2022 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122468693","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}
Pub Date : 2022-05-02DOI: 10.1109/asmc54647.2022.9792510
R. Mair, G. A. Antonelli, M. Mehendale, P. Mukundhan, Beth May, Karen Terry, N. Brandt, Xiaoyue Phillip Huang, Tong Zhao
Thermal management is a critical aspect of integrated device design and manufacture. Time Domain Thermoreflectance (TDTR) is a powerful tool for the characterization of thermal transport in thin films and multi-layer stacks. In this paper, we present successful extension of in-line non-contact, non-destructive picosecond ultrasonic metrology for simultaneous measurements of layer thickness and thermal properties.
{"title":"Non-Contact, In-Line Thermal Characterization Capability with Time Domain Thermoreflectance","authors":"R. Mair, G. A. Antonelli, M. Mehendale, P. Mukundhan, Beth May, Karen Terry, N. Brandt, Xiaoyue Phillip Huang, Tong Zhao","doi":"10.1109/asmc54647.2022.9792510","DOIUrl":"https://doi.org/10.1109/asmc54647.2022.9792510","url":null,"abstract":"Thermal management is a critical aspect of integrated device design and manufacture. Time Domain Thermoreflectance (TDTR) is a powerful tool for the characterization of thermal transport in thin films and multi-layer stacks. In this paper, we present successful extension of in-line non-contact, non-destructive picosecond ultrasonic metrology for simultaneous measurements of layer thickness and thermal properties.","PeriodicalId":436890,"journal":{"name":"2022 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125171250","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}
Pub Date : 2022-05-02DOI: 10.1109/asmc54647.2022.9792501
I. Azevedo, Matthew A. Hurley, D. Mosher
A new fault detection data model was developed for High Density Plasma (HDP) SiN deposition processing to detect copper migration caused by silane (SiH4) introduction into the chamber prior to the deposition step. Before the development of this model, copper (Cu) migration could not be detected until electrical test. Potential product exposure was high due to the elapsed time between occurrence and detection. The model provides an automated detection system, reducing the magnitude of product exposure from repeat instances of silane bursting.
{"title":"Implementation of an OES System to Detect Silane Bursting During HDP SiN Film Deposition","authors":"I. Azevedo, Matthew A. Hurley, D. Mosher","doi":"10.1109/asmc54647.2022.9792501","DOIUrl":"https://doi.org/10.1109/asmc54647.2022.9792501","url":null,"abstract":"A new fault detection data model was developed for High Density Plasma (HDP) SiN deposition processing to detect copper migration caused by silane (SiH4) introduction into the chamber prior to the deposition step. Before the development of this model, copper (Cu) migration could not be detected until electrical test. Potential product exposure was high due to the elapsed time between occurrence and detection. The model provides an automated detection system, reducing the magnitude of product exposure from repeat instances of silane bursting.","PeriodicalId":436890,"journal":{"name":"2022 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133530396","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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