@article{Zhou2026, 
author = {Ruifu Zhou and Hyeon-seo Do and Jang-Sik Lee},
title = {Toward high-layer 3D hafnia ferroelectric stacks for neuromorphic computing: manufacturing insights and integration challenges},
year = {2026},
journal = {International Journal of Extreme Manufacturing},
volume = {8},
number = {3},
keywords = {hafnium oxide ferroelectrics, neuromorphic computing, 3D integration},
url = {https://www.sciopen.com/article/10.1088/2631-7990/ae366f},
doi = {10.1088/2631-7990/ae366f},
abstract = {Ferroelectric hafnium-oxide (HfO2) films have revitalized interest in brain-inspired hardware because of their high scalability, compatibility with complementary metal-oxide-semiconductor (CMOS) processes, and suitability for three-dimensional (3D) architectures. This review first analyses the origin, deposition routes, and performance of hafnia-based devices, including ferroelectric field-effect transistor, ferroelectric tunnelling junction and ferroelectric capacitor. As artificial intelligence (AI) continues to advance, the demand for higher memory density becomes increasingly critical. This review presents hafnia-based devices and arrays in both planar and 3D architectures. In 3D structures, the review discusses the principal integration constraints—back-end-of-line (BEOL)-compatible crystallization, conformal atomic layer deposition (ALD) with controlled phase and defects in high-aspect-ratio features, and cross-layer stress together with layer-to-layer variability/disturbance,which collectively determine stackable scalability and influence energy efficiency and training stability, thereby pointing toward compact, energy-efficient, and scalable 3D neuromorphic hardware based on hafnia ferroelectrics.}
}