Towards Artificial Intelligence Hardware With 3D Integrated Ferroelectric Transistors

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.