Towards Artificial Intelligence Hardware With 3D Integrated Ferroelectric Transistors
Year: 2026
Type: article
Abstract: ABSTRACT The increasing energy and bandwidth demand of modern AI workloads highlight the need for hardware that mitigates the data‐movement bottleneck of von Neumann architectures. Compute‐in‐memory and neuromorphic systems offer a compelling solution, yet reliable multi‐tier 3D integration of analog synaptic devices remains challenging. Here, we report a monolithic 3D (M3D) integration platform that vertically stacks In‐Ga‐Zn‐O (IGZO) access transistors and Hf 0.5 Zr 0.5 O (HZO)‐based ferroelectric transistors to realize compact, energy‐efficient neuromorphic hardware. Two‐tier and four‐tier IGZO/ferroelectric field effect transistors (FeFET) architectures were fabricated with excellent structural integrity, uniform elemental profiles, and preserved orthorhombic HZO ferroelectricity across all tiers. The devices exhibit reproducible switching, >10‐year retention, endurance up to 10 1 1 cycles, and stable multilevel conductance states suitable for synaptic computing. Mapping device characteristics to a convolutional neural network (CNN) for Canadian Institute for Advanced Research (CIFAR‐10) inference yields 95.0% (tier‐2) and 95.5% (tier‐4) accuracy, approaching the 96.1% software baseline. Analog‐domain convolution was further demonstrated by encoding kernel weights into FeFET conductance states for edge‐aware image processing. These results establish M3D‐integrated FeFETs as a scalable and reliable platform for next‐generation compute‐in‐memory and neuromorphic vision applications.
Source: Small
DOI: 10.1002/smll.73295
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $5,790
OpenAlex ID: W7155043135