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Data-Driven Approach to Modeling Microfabricated Chemical Sensor Manufacturing.
Chew, Bradley S; Trinh, Nhi N; Koch, Dylan T; Borras, Eva; LeVasseur, Michael K; Simms, Leslie A; McCartney, Mitchell M; Gibson, Patrick; Kenyon, Nicholas J; Davis, Cristina E.
Afiliación
  • Chew BS; Department of Mechanical and Aerospace Engineering, One Shields Avenue, University of California Davis, Davis, California 95616, United States.
  • Trinh NN; UC Davis Lung Center, One Shields Avenue, University of California Davis, Davis, California 95616, United States.
  • Koch DT; Department of Biomedical Engineering, One Shields Avenue, University of California Davis, Davis, California 95616, United States.
  • Borras E; UC Davis Lung Center, One Shields Avenue, University of California Davis, Davis, California 95616, United States.
  • LeVasseur MK; Department of Electrical and Computer Engineering, One Shields Avenue, University of California Davis, Davis, California 95616, United States.
  • Simms LA; UC Davis Lung Center, One Shields Avenue, University of California Davis, Davis, California 95616, United States.
  • McCartney MM; Department of Mechanical and Aerospace Engineering, One Shields Avenue, University of California Davis, Davis, California 95616, United States.
  • Gibson P; UC Davis Lung Center, One Shields Avenue, University of California Davis, Davis, California 95616, United States.
  • Kenyon NJ; Department of Mechanical and Aerospace Engineering, One Shields Avenue, University of California Davis, Davis, California 95616, United States.
  • Davis CE; UC Davis Lung Center, One Shields Avenue, University of California Davis, Davis, California 95616, United States.
Anal Chem ; 96(1): 364-372, 2024 Jan 09.
Article en En | MEDLINE | ID: mdl-38156894
ABSTRACT
We have developed a statistical model-based approach to the quality analysis (QA) and quality control (QC) of a gas micro pre-concentrator chip (µPC) performance when manufactured at scale for chemical and biochemical analysis of volatile organic compounds (VOCs). To test the proposed model, a medium-sized university-led production batch of 30 wafers of chips were subjected to rigorous chemical performance testing. We quantitatively report the outcomes of each manufacturing process step leading to the final functional chemical sensor chip. We implemented a principal component analysis (PCA) model to score individual chip chemical performance, and we observed that the first two principal components represent 74.28% of chemical testing variance with 111 of 118 viable chips falling into the 95% confidence interval. Chemical performance scores and chip manufacturing data were analyzed using a multivariate regression model to determine the most influential manufacturing parameters and steps. In our analysis, we find the amount of sorbent mass present in the chip (variable importance score = 2.6) and heater and the RTD resistance values (variable importance score = 1.1) to be the manufacturing parameters with the greatest impact on chemical performance. Other non-obvious latent manufacturing parameters also had quantified influence. Statistical distributions for each manufacturing step will allow future large-scale production runs to be statistically sampled during production to perform QA/QC in a real-time environment. We report this study as the first data-driven, model-based production of a microfabricated chemical sensor.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Anal Chem Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Anal Chem Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos