A data-driven framework combining a GA-BP neural network for resistance prediction and a CPSO algorithm for inverse parameter design optimizes the screen-printing process of thick-film resistors, controlling resistance deviation within 5%. (IMAGE)

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A data-driven framework combining a GA-BP neural network for resistance prediction and a CPSO algorithm for inverse parameter design optimizes the screen-printing process of thick-film resistors, controlling resistance deviation within 5%.

Caption

A data-driven framework combining a GA-BP neural network for resistance prediction and a CPSO algorithm for inverse parameter design optimizes the screen-printing process of thick-film resistors, controlling resistance deviation within 5%.

Credit

Yu Sun, Youyang Wang, Jiani Xue, Xingyao Zhang, Xiyi Liao, Wenhua Gu / Nanjing University of Science and Technology

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CC BY

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