AZoM

Machine Learning Guides ALD to Predict Better Hafnium Oxide Film Quality (opens in new tab)

Researchers developed a deep neural network framework that predicts hafnium oxide thin-film thickness, refractive index, and wet etch rate from atomic-layer-deposition process conditions and wafer location. The model generated process-property maps that could accelerate ALD optimization while supporting future digital twin-guided semiconductor manufacturing.

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