AI-Driven Inverse Design of Complex Oxide Thin Films for Semiconductor Devices: A Case Study of Hf–Zr–O

Bridging generative foundation models with nonequilibrium 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, nonequilibrium thin-film growth. By experiment-coupled validation, IDEAL provides a transferable and generalizable route to the precision synthesis of next-generation semiconductor dielectrics.