This paper investigates the potential of a fully behavioral approach for the generation of accurate models of digital IC buffers based on conventional kernel regressions. The proposed approach does not assume a specific model structure like the classical two-piece model representation which has been massively used in literature, offering a promising and viable alternative to facilitate the modeling of nonlinear electrical devices. The collected results represent a first proof-of-concept, aimed at demonstrating the strengths of the proposed alternative modeling approach.

Modeling of IC Buffers from Channel Responses Via Machine Learning Kernel Regression / Trinchero, Riccardo; Bradde, Tommaso; Telescu, Mihai; Stievano, Igor S.. - STAMPA. - (2024). (Intervento presentato al convegno 2024 IEEE 28th Workshop on Signal and Power Integrity (SPI) tenutosi a Lisbon (Portugal) nel May 12-15 2024) [10.1109/spi60975.2024.10539199].

Modeling of IC Buffers from Channel Responses Via Machine Learning Kernel Regression

Trinchero, Riccardo;Bradde, Tommaso;Stievano, Igor S.
2024

Abstract

This paper investigates the potential of a fully behavioral approach for the generation of accurate models of digital IC buffers based on conventional kernel regressions. The proposed approach does not assume a specific model structure like the classical two-piece model representation which has been massively used in literature, offering a promising and viable alternative to facilitate the modeling of nonlinear electrical devices. The collected results represent a first proof-of-concept, aimed at demonstrating the strengths of the proposed alternative modeling approach.
2024
979-8-3503-8293-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2989242