Condition monitoring and prediction of solution quality during a copper electroplating process

Abstract This paper presents a method for the monitoring and prediction of the electrolyte quality during the process of copper electroplating. This is important in industry, as any deviation in the solution quality leads to a deterioration of the quality of the processed products. The aim of the st...
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Gespeichert in:

Autor*in:	Granados, Gerardo Emanuel [verfasserIn] Lacroix, Loïc [verfasserIn] Medjaher, Kamal [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Condition monitoring Fault prognostics Prognostics and health management (PHM) Remaining useful life Copper electroplating process

Übergeordnetes Werk:	Enthalten in: Journal of intelligent manufacturing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990, 31(2018), 2 vom: 27. Sept., Seite 285-300
Übergeordnetes Werk:	volume:31 ; year:2018 ; number:2 ; day:27 ; month:09 ; pages:285-300

Links:	Volltext

DOI / URN:	10.1007/s10845-018-1445-4

Katalog-ID:	SPR013688391

Internformat


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520			\|a Abstract This paper presents a method for the monitoring and prediction of the electrolyte quality during the process of copper electroplating. This is important in industry, as any deviation in the solution quality leads to a deterioration of the quality of the processed products. The aim of the study is to identify some physical parameters that are representative of the quality variation during the deposition process. These parameters are then tracked online to continuously assess the solution quality and predict its remaining useful life. To do this, the process behavior is first characterized to derive a nominal model and to identify the physical parameters that can be used to describe the aging variation in the electrolyte quality. The aging model is then explored to assess the current level of the solution quality and to predict its remaining useful life. The proposed method is verified using real data acquired from a specifically designed test bench. The obtained results reveal the efficiency of the method.
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author_variant	g e g ge geg l l ll k m km
matchkey_str	article:15728145:2018----::odtomntrnadrdcinfouinultdrnaop
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bklnumber	52.72
publishDate	2018
allfields	10.1007/s10845-018-1445-4 doi (DE-627)SPR013688391 (SPR)s10845-018-1445-4-e DE-627 ger DE-627 rakwb eng 004 620 ASE 52.72 bkl Granados, Gerardo Emanuel verfasserin aut Condition monitoring and prediction of solution quality during a copper electroplating process 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents a method for the monitoring and prediction of the electrolyte quality during the process of copper electroplating. This is important in industry, as any deviation in the solution quality leads to a deterioration of the quality of the processed products. The aim of the study is to identify some physical parameters that are representative of the quality variation during the deposition process. These parameters are then tracked online to continuously assess the solution quality and predict its remaining useful life. To do this, the process behavior is first characterized to derive a nominal model and to identify the physical parameters that can be used to describe the aging variation in the electrolyte quality. The aging model is then explored to assess the current level of the solution quality and to predict its remaining useful life. The proposed method is verified using real data acquired from a specifically designed test bench. The obtained results reveal the efficiency of the method. Condition monitoring (dpeaa)DE-He213 Fault prognostics (dpeaa)DE-He213 Prognostics and health management (PHM) (dpeaa)DE-He213 Remaining useful life (dpeaa)DE-He213 Copper electroplating process (dpeaa)DE-He213 Lacroix, Loïc verfasserin aut Medjaher, Kamal verfasserin aut Enthalten in Journal of intelligent manufacturing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 31(2018), 2 vom: 27. Sept., Seite 285-300 (DE-627)315293519 (DE-600)2015292-9 1572-8145 nnns volume:31 year:2018 number:2 day:27 month:09 pages:285-300 https://dx.doi.org/10.1007/s10845-018-1445-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.72 ASE AR 31 2018 2 27 09 285-300
spelling	10.1007/s10845-018-1445-4 doi (DE-627)SPR013688391 (SPR)s10845-018-1445-4-e DE-627 ger DE-627 rakwb eng 004 620 ASE 52.72 bkl Granados, Gerardo Emanuel verfasserin aut Condition monitoring and prediction of solution quality during a copper electroplating process 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents a method for the monitoring and prediction of the electrolyte quality during the process of copper electroplating. This is important in industry, as any deviation in the solution quality leads to a deterioration of the quality of the processed products. The aim of the study is to identify some physical parameters that are representative of the quality variation during the deposition process. These parameters are then tracked online to continuously assess the solution quality and predict its remaining useful life. To do this, the process behavior is first characterized to derive a nominal model and to identify the physical parameters that can be used to describe the aging variation in the electrolyte quality. The aging model is then explored to assess the current level of the solution quality and to predict its remaining useful life. The proposed method is verified using real data acquired from a specifically designed test bench. The obtained results reveal the efficiency of the method. Condition monitoring (dpeaa)DE-He213 Fault prognostics (dpeaa)DE-He213 Prognostics and health management (PHM) (dpeaa)DE-He213 Remaining useful life (dpeaa)DE-He213 Copper electroplating process (dpeaa)DE-He213 Lacroix, Loïc verfasserin aut Medjaher, Kamal verfasserin aut Enthalten in Journal of intelligent manufacturing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 31(2018), 2 vom: 27. Sept., Seite 285-300 (DE-627)315293519 (DE-600)2015292-9 1572-8145 nnns volume:31 year:2018 number:2 day:27 month:09 pages:285-300 https://dx.doi.org/10.1007/s10845-018-1445-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.72 ASE AR 31 2018 2 27 09 285-300
allfields_unstemmed	10.1007/s10845-018-1445-4 doi (DE-627)SPR013688391 (SPR)s10845-018-1445-4-e DE-627 ger DE-627 rakwb eng 004 620 ASE 52.72 bkl Granados, Gerardo Emanuel verfasserin aut Condition monitoring and prediction of solution quality during a copper electroplating process 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents a method for the monitoring and prediction of the electrolyte quality during the process of copper electroplating. This is important in industry, as any deviation in the solution quality leads to a deterioration of the quality of the processed products. The aim of the study is to identify some physical parameters that are representative of the quality variation during the deposition process. These parameters are then tracked online to continuously assess the solution quality and predict its remaining useful life. To do this, the process behavior is first characterized to derive a nominal model and to identify the physical parameters that can be used to describe the aging variation in the electrolyte quality. The aging model is then explored to assess the current level of the solution quality and to predict its remaining useful life. The proposed method is verified using real data acquired from a specifically designed test bench. The obtained results reveal the efficiency of the method. Condition monitoring (dpeaa)DE-He213 Fault prognostics (dpeaa)DE-He213 Prognostics and health management (PHM) (dpeaa)DE-He213 Remaining useful life (dpeaa)DE-He213 Copper electroplating process (dpeaa)DE-He213 Lacroix, Loïc verfasserin aut Medjaher, Kamal verfasserin aut Enthalten in Journal of intelligent manufacturing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 31(2018), 2 vom: 27. Sept., Seite 285-300 (DE-627)315293519 (DE-600)2015292-9 1572-8145 nnns volume:31 year:2018 number:2 day:27 month:09 pages:285-300 https://dx.doi.org/10.1007/s10845-018-1445-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.72 ASE AR 31 2018 2 27 09 285-300
allfieldsGer	10.1007/s10845-018-1445-4 doi (DE-627)SPR013688391 (SPR)s10845-018-1445-4-e DE-627 ger DE-627 rakwb eng 004 620 ASE 52.72 bkl Granados, Gerardo Emanuel verfasserin aut Condition monitoring and prediction of solution quality during a copper electroplating process 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents a method for the monitoring and prediction of the electrolyte quality during the process of copper electroplating. This is important in industry, as any deviation in the solution quality leads to a deterioration of the quality of the processed products. The aim of the study is to identify some physical parameters that are representative of the quality variation during the deposition process. These parameters are then tracked online to continuously assess the solution quality and predict its remaining useful life. To do this, the process behavior is first characterized to derive a nominal model and to identify the physical parameters that can be used to describe the aging variation in the electrolyte quality. The aging model is then explored to assess the current level of the solution quality and to predict its remaining useful life. The proposed method is verified using real data acquired from a specifically designed test bench. The obtained results reveal the efficiency of the method. Condition monitoring (dpeaa)DE-He213 Fault prognostics (dpeaa)DE-He213 Prognostics and health management (PHM) (dpeaa)DE-He213 Remaining useful life (dpeaa)DE-He213 Copper electroplating process (dpeaa)DE-He213 Lacroix, Loïc verfasserin aut Medjaher, Kamal verfasserin aut Enthalten in Journal of intelligent manufacturing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 31(2018), 2 vom: 27. Sept., Seite 285-300 (DE-627)315293519 (DE-600)2015292-9 1572-8145 nnns volume:31 year:2018 number:2 day:27 month:09 pages:285-300 https://dx.doi.org/10.1007/s10845-018-1445-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.72 ASE AR 31 2018 2 27 09 285-300
allfieldsSound	10.1007/s10845-018-1445-4 doi (DE-627)SPR013688391 (SPR)s10845-018-1445-4-e DE-627 ger DE-627 rakwb eng 004 620 ASE 52.72 bkl Granados, Gerardo Emanuel verfasserin aut Condition monitoring and prediction of solution quality during a copper electroplating process 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents a method for the monitoring and prediction of the electrolyte quality during the process of copper electroplating. This is important in industry, as any deviation in the solution quality leads to a deterioration of the quality of the processed products. The aim of the study is to identify some physical parameters that are representative of the quality variation during the deposition process. These parameters are then tracked online to continuously assess the solution quality and predict its remaining useful life. To do this, the process behavior is first characterized to derive a nominal model and to identify the physical parameters that can be used to describe the aging variation in the electrolyte quality. The aging model is then explored to assess the current level of the solution quality and to predict its remaining useful life. The proposed method is verified using real data acquired from a specifically designed test bench. The obtained results reveal the efficiency of the method. Condition monitoring (dpeaa)DE-He213 Fault prognostics (dpeaa)DE-He213 Prognostics and health management (PHM) (dpeaa)DE-He213 Remaining useful life (dpeaa)DE-He213 Copper electroplating process (dpeaa)DE-He213 Lacroix, Loïc verfasserin aut Medjaher, Kamal verfasserin aut Enthalten in Journal of intelligent manufacturing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 31(2018), 2 vom: 27. Sept., Seite 285-300 (DE-627)315293519 (DE-600)2015292-9 1572-8145 nnns volume:31 year:2018 number:2 day:27 month:09 pages:285-300 https://dx.doi.org/10.1007/s10845-018-1445-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.72 ASE AR 31 2018 2 27 09 285-300
language	English
source	Enthalten in Journal of intelligent manufacturing 31(2018), 2 vom: 27. Sept., Seite 285-300 volume:31 year:2018 number:2 day:27 month:09 pages:285-300
sourceStr	Enthalten in Journal of intelligent manufacturing 31(2018), 2 vom: 27. Sept., Seite 285-300 volume:31 year:2018 number:2 day:27 month:09 pages:285-300
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Condition monitoring Fault prognostics Prognostics and health management (PHM) Remaining useful life Copper electroplating process
dewey-raw	004
isfreeaccess_bool	false
container_title	Journal of intelligent manufacturing
authorswithroles_txt_mv	Granados, Gerardo Emanuel @@aut@@ Lacroix, Loïc @@aut@@ Medjaher, Kamal @@aut@@
publishDateDaySort_date	2018-09-27T00:00:00Z
hierarchy_top_id	315293519
dewey-sort	14
id	SPR013688391
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR013688391</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111003439.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2018 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10845-018-1445-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR013688391</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10845-018-1445-4-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="a">620</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">52.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Granados, Gerardo Emanuel</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Condition monitoring and prediction of solution quality during a copper electroplating process</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper presents a method for the monitoring and prediction of the electrolyte quality during the process of copper electroplating. This is important in industry, as any deviation in the solution quality leads to a deterioration of the quality of the processed products. The aim of the study is to identify some physical parameters that are representative of the quality variation during the deposition process. These parameters are then tracked online to continuously assess the solution quality and predict its remaining useful life. To do this, the process behavior is first characterized to derive a nominal model and to identify the physical parameters that can be used to describe the aging variation in the electrolyte quality. The aging model is then explored to assess the current level of the solution quality and to predict its remaining useful life. The proposed method is verified using real data acquired from a specifically designed test bench. 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author	Granados, Gerardo Emanuel
spellingShingle	Granados, Gerardo Emanuel ddc 004 bkl 52.72 misc Condition monitoring misc Fault prognostics misc Prognostics and health management (PHM) misc Remaining useful life misc Copper electroplating process Condition monitoring and prediction of solution quality during a copper electroplating process
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topic_title	004 620 ASE 52.72 bkl Condition monitoring and prediction of solution quality during a copper electroplating process Condition monitoring (dpeaa)DE-He213 Fault prognostics (dpeaa)DE-He213 Prognostics and health management (PHM) (dpeaa)DE-He213 Remaining useful life (dpeaa)DE-He213 Copper electroplating process (dpeaa)DE-He213
topic	ddc 004 bkl 52.72 misc Condition monitoring misc Fault prognostics misc Prognostics and health management (PHM) misc Remaining useful life misc Copper electroplating process
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title	Condition monitoring and prediction of solution quality during a copper electroplating process
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title_full	Condition monitoring and prediction of solution quality during a copper electroplating process
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title_sort	condition monitoring and prediction of solution quality during a copper electroplating process
title_auth	Condition monitoring and prediction of solution quality during a copper electroplating process
abstract	Abstract This paper presents a method for the monitoring and prediction of the electrolyte quality during the process of copper electroplating. This is important in industry, as any deviation in the solution quality leads to a deterioration of the quality of the processed products. The aim of the study is to identify some physical parameters that are representative of the quality variation during the deposition process. These parameters are then tracked online to continuously assess the solution quality and predict its remaining useful life. To do this, the process behavior is first characterized to derive a nominal model and to identify the physical parameters that can be used to describe the aging variation in the electrolyte quality. The aging model is then explored to assess the current level of the solution quality and to predict its remaining useful life. The proposed method is verified using real data acquired from a specifically designed test bench. The obtained results reveal the efficiency of the method.
abstractGer	Abstract This paper presents a method for the monitoring and prediction of the electrolyte quality during the process of copper electroplating. This is important in industry, as any deviation in the solution quality leads to a deterioration of the quality of the processed products. The aim of the study is to identify some physical parameters that are representative of the quality variation during the deposition process. These parameters are then tracked online to continuously assess the solution quality and predict its remaining useful life. To do this, the process behavior is first characterized to derive a nominal model and to identify the physical parameters that can be used to describe the aging variation in the electrolyte quality. The aging model is then explored to assess the current level of the solution quality and to predict its remaining useful life. The proposed method is verified using real data acquired from a specifically designed test bench. The obtained results reveal the efficiency of the method.
abstract_unstemmed	Abstract This paper presents a method for the monitoring and prediction of the electrolyte quality during the process of copper electroplating. This is important in industry, as any deviation in the solution quality leads to a deterioration of the quality of the processed products. The aim of the study is to identify some physical parameters that are representative of the quality variation during the deposition process. These parameters are then tracked online to continuously assess the solution quality and predict its remaining useful life. To do this, the process behavior is first characterized to derive a nominal model and to identify the physical parameters that can be used to describe the aging variation in the electrolyte quality. The aging model is then explored to assess the current level of the solution quality and to predict its remaining useful life. The proposed method is verified using real data acquired from a specifically designed test bench. The obtained results reveal the efficiency of the method.
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container_issue	2
title_short	Condition monitoring and prediction of solution quality during a copper electroplating process
url	https://dx.doi.org/10.1007/s10845-018-1445-4
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author2	Lacroix, Loïc Medjaher, Kamal
author2Str	Lacroix, Loïc Medjaher, Kamal
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doi_str	10.1007/s10845-018-1445-4
up_date	2024-07-03T21:29:18.740Z
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Condition monitoring and prediction of solution quality during a copper electroplating process

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