TY - GEN
T1 - Machine Learning based Performance Prediction of Microcontrollers using Speed Monitors
AU - Cantoro, Riccardo
AU - Huch, Martin
AU - Kilian, Tobias
AU - Martone, Raffaele
AU - Schlichtmann, Ulf
AU - Squillero, Giovanni
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - During the manufacturing process, electronic devices are thoroughly tested for defects. However, testing for well-known fault models, such as stuck-At and transition delay, may not be sufficient for an effective performance screening. In modern devices, Design-for-Testability features embedded at design time can allow the tester to apply stimuli and measure different critical parameters. We propose to use some of these structures, namely the speed monitors, to predict the maximum operating speed, and screen out under-performing devices. We design a complete methodology, from the extraction of robust labels, through a machine-learning algorithm, down to a post-processing step, able to meet the quality standards imposed by industry. Experimental results using real production data demonstrate the feasibility of the approach.
AB - During the manufacturing process, electronic devices are thoroughly tested for defects. However, testing for well-known fault models, such as stuck-At and transition delay, may not be sufficient for an effective performance screening. In modern devices, Design-for-Testability features embedded at design time can allow the tester to apply stimuli and measure different critical parameters. We propose to use some of these structures, namely the speed monitors, to predict the maximum operating speed, and screen out under-performing devices. We design a complete methodology, from the extraction of robust labels, through a machine-learning algorithm, down to a post-processing step, able to meet the quality standards imposed by industry. Experimental results using real production data demonstrate the feasibility of the approach.
KW - Machine Learning
KW - Performance Screening
KW - Speed Monitors
UR - http://www.scopus.com/inward/record.url?scp=85100138780&partnerID=8YFLogxK
U2 - 10.1109/ITC44778.2020.9325253
DO - 10.1109/ITC44778.2020.9325253
M3 - Conference contribution
AN - SCOPUS:85100138780
T3 - Proceedings - International Test Conference
BT - 2020 IEEE International Test Conference, ITC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Test Conference, ITC 2020
Y2 - 1 November 2020 through 6 November 2020
ER -