Paper
AI-driven Inverse Design of Complex Oxide Thin Films for Semiconductor Devices
Authors
Bonwook Gu, Trinh Ngoc Le, Wonjoong Kim, Zunair Masroor, Han-Bo-Ram Lee
Abstract
Bridging generative foundation models with non-equilibrium thin-film synthesis remains a central challenge, limiting the practical impact of AI-driven materials discovery on semiconductor dielectrics. Here, we introduce IDEAL (Inverse Design for Experimental Atomic Layers), an inverse-design platform that links generative diffusion models, machine learning interatomic potentials, and graph neural network property predictors with atomic layer deposition (ALD). We demonstrate IDEAL using the Hf-Zr-O system as a stringent benchmark for semiconductor-relevant complex oxides. The platform statistically enumerates thermodynamically plausible structures and constructs a composition-structure-property map. Crucially, it identifies a narrow composition window where low-energy tetragonal and orthorhombic phases cluster, revealing trade-offs between band gap and dielectric response. Experimental validation using atomic layer modulation (ALM) corroborates these predictions, demonstrating predictive guidance under realistic, non-equilibrium thin-film growth. By experimentally closing the loop, IDEAL provides a transferable and generalizable route to the precision synthesis of next-generation semiconductor dielectrics.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.09744v1</id>\n <title>AI-driven Inverse Design of Complex Oxide Thin Films for Semiconductor Devices</title>\n <updated>2026-03-10T14:48:35Z</updated>\n <link href='https://arxiv.org/abs/2603.09744v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.09744v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Bridging generative foundation models with non-equilibrium thin-film synthesis remains a central challenge, limiting the practical impact of AI-driven materials discovery on semiconductor dielectrics. Here, we introduce IDEAL (Inverse Design for Experimental Atomic Layers), an inverse-design platform that links generative diffusion models, machine learning interatomic potentials, and graph neural network property predictors with atomic layer deposition (ALD). We demonstrate IDEAL using the Hf-Zr-O system as a stringent benchmark for semiconductor-relevant complex oxides. The platform statistically enumerates thermodynamically plausible structures and constructs a composition-structure-property map. Crucially, it identifies a narrow composition window where low-energy tetragonal and orthorhombic phases cluster, revealing trade-offs between band gap and dielectric response. Experimental validation using atomic layer modulation (ALM) corroborates these predictions, demonstrating predictive guidance under realistic, non-equilibrium thin-film growth. By experimentally closing the loop, IDEAL provides a transferable and generalizable route to the precision synthesis of next-generation semiconductor dielectrics.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cond-mat.mtrl-sci'/>\n <published>2026-03-10T14:48:35Z</published>\n <arxiv:comment>25 pages, 7 figures</arxiv:comment>\n <arxiv:primary_category term='cond-mat.mtrl-sci'/>\n <author>\n <name>Bonwook Gu</name>\n </author>\n <author>\n <name>Trinh Ngoc Le</name>\n </author>\n <author>\n <name>Wonjoong Kim</name>\n </author>\n <author>\n <name>Zunair Masroor</name>\n </author>\n <author>\n <name>Han-Bo-Ram Lee</name>\n </author>\n </entry>"
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