Microelectronic Packaging: Electrical Interconnections

Charles, Harry K.; Sujan, G.K.

doi:10.1016/b978-0-12-803581-8.01810-5

Reference Module in Materials Science and Materials Engineering 2016

DOI: 10.1016/b978-0-12-803581-8.01810-5

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Microelectronic Packaging: Electrical Interconnections

Harry K. Charles¹,

G.K. Sujan²

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2022

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Generative design of stable semiconductor materials using deep learning and density functional theory

et al. 2022

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Semiconductor device technology has greatly developed in complexity since discovering the bipolar transistor. In this work, we developed a computational pipeline to discover stable semiconductors by combining generative adversarial networks (GAN), classifiers, and high-throughput first-principles calculations. We used CubicGAN, a GAN-based algorithm for generating cubic materials and developed a classifier to screen the semiconductors and studied their stability using first principles. We found 12 stable AA$${}^{\prime}$$ ′ MH6 semiconductors in the F-43m space group including BaNaRhH6, BaSrZnH6, BaCsAlH6, SrTlIrH6, KNaNiH6, NaYRuH6, CsKSiH6, CaScMnH6, YZnMnH6, NaZrMnH6, AgZrMnH6, and ScZnMnH6. Previous research reported that five AA$${}^{\prime}$$ ′ IrH6 semiconductors with the same space group were synthesized. Our research shows that AA$${}^{\prime}$$ ′ MnH6 and NaYRuH6 semiconductors have considerably different properties compared to the rest of the AA$${}^{\prime}$$ ′ MH6 semiconductors. Based on the accurate hybrid functional calculations, AA$${}^{\prime}$$ ′ MH6 semiconductors are found to be wide-bandgap semiconductors. Moreover, BaSrZnH6 and KNaNiH6 are direct-bandgap semiconductors, whereas others exhibit indirect bandgaps.

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Generative design of stable semiconductor materials using deep learning and density functional theory

et al. 2022

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Microelectronic Packaging: Electrical Interconnections

Cited by 1 publication

References 24 publications

Generative design of stable semiconductor materials using deep learning and density functional theory

Generative design of stable semiconductor materials using deep learning and density functional theory

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