- author = "Monika Grezer and Marina Topa and Emilian David and Andi Buzo and Georg Pelz",
- title = "Using Symbolic Regression Metamodels for Integrated Circuits Production",
- booktitle = "2024 15th International Conference on Advanced Semiconductor Devices and Microsystems (ASDAM)",
- year = "2024",
- pages = "18--21",
- month = oct,
- keywords = "genetic algorithms, genetic programming, Micromechanical devices, Semiconductor devices, Electronics industry, Production, Gaussian processes, Machine learning, Product development, Integrated circuit modelling, Optimisation",
- ISSN = "2474-9737",
-
DOI = "
doi:10.1109/ASDAM63148.2024.10844676",
- abstract = "The paper introduces a Machine Learning QLattice metamodel that can be used as a more efficient substitute for the circuit simulators in the integrated circuit industry with less compute effort. QLattice represents a Symbolic Regression metamodel that is based on Genetic Programming. The purpose of this metamodel is to find the best mathematical formula that achieves the optimal correlation between the inputs and the output. We made a comparative analysis between two regression metamodels, namely QLattice and Gaussian Process, on synthetic functions that emulate real circuits behaviour, as well as on the datasets used for real-life product development.",
-
notes = "Also known as \cite{10844676}
Technical University of Cluj-Napoca, Romania",
