Data-driven Parameters Tuning for Predictive Performance Improvement of Wire Bonder Multi-body Model

Xiaodong Cheng, Alessandro Di Bucchianico, Najmeh Javanmardi, Matthijs de Jong, Emil Lykke Diget, Colin Please, Domenico Lahaye, Qiyao Peng, Cordula Reisch, Davide Sclosa

Research output: Book/reportReportResearch

Abstract

This report describes work performed during SWI 2023 at the Univer-
sity of Groningen in relation with Problem 1 posed by the company
ASMPT.
ASMPT makes a very large number of different machines for manufac-
turing of electronic devices. They have detailed simulation software of
one of these machines and they compare the results of this with phys-
ical experimental results. There is a significant difference between the
simulated and measured data, and it is the goal of this work to study
how to estimate the parameters in the simulation model using the ex-
perimentally measured frequency response.
First, two toy models are studied to understand the challenges of pa-
rameter estimation in the frequency domain. Later, optimization meth-
ods are applied. Several different approaches of reducing the dimen-
sionality of the parameter space are explored, including determining
the parameter sensitivity. A suggestion for increasing the detail of the
model, specifically related to the machine base, is also outlined.
In the summary, we supply a discussion of the key insights we gained
during the week.
Original languageEnglish
DOIs
Publication statusPublished - 2024

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