ScholarWorks@성균관대학교

초록

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 Hf0.5Zr0.5O (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.

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