Statistical supervised learning with engineering data: A case study of low frequency noise measured on semiconductor devices
2022 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 120, no 9-10, p. 5835-5853Article in journal (Refereed) Published
Abstract [en]
Our practical motivation is the analysis of potential correlations between spectral noise current and threshold voltage from common on-wafer MOSFETs. The usual strategy leads to the use of standard techniques based on Normal linear regression easily accessible in all statistical software (both free or commercial). However, these statistical methods are not appropriate because the assumptions they lie on are not met. More sophisticated methods are required. A new strategy based on the most novel nonparametric techniques which are data-driven and thus free from questionable parametric assumptions is proposed. A backfitting algorithm accounting for random effects and nonparametric regression is designed and implemented. The nature of the correlation between threshold voltage and noise is examined by conducting a statistical test, which is based on a novel technique that summarizes in a color map all the relevant information of the data. The way the results are presented in the plot makes it easy for a non-expert in data analysis to understand what is underlying. The good performance of the method is proven through simulations and it is applied to a data case in a field where these modern statistical techniques are novel and result very efficient.
Place, publisher, year, edition, pages
Springer Nature Switzerland AG , 2022. Vol. 120, no 9-10, p. 5835-5853
Keywords [en]
1/f Noise, Backfitting algorithm, Bootstrap, MOSFET, SiZer Map, Statistical modeling
National Category
Probability Theory and Statistics Signal Processing Nano Technology
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-21025DOI: 10.1007/s00170-022-08949-zISI: 000777644800002Scopus ID: 2-s2.0-85128289200OAI: oai:DiVA.org:his-21025DiVA, id: diva2:1649262
Note
CC BY 4.0
© 2022 Springer Nature Switzerland AG. Part of Springer Nature.
Published: 04 April 2022
Corresponding author: María Luz Gámiz
Funding for open access charge: Universidad de Granada / CBUA
This work was supported in part by the Spanish Ministry of Science and Innovation through grants number RTI2018-099723-B-I00, and PID2020-120217RB-I00; the Spanish Junta de Andalucíathrough grants number B-FQM-284-UGR20 and B-CTS-184-UGR20; and the IMAG-Maria de Maeztu grant CEX2020-001105-/AEI/10.13039/501100011033.
2022-04-042022-04-042022-07-12Bibliographically approved