Publication date: 21st July 2025
We report the development of ferroelectric and resistive memory arrays fabricated via atomic layer deposition (ALD) for neuromorphic and in-memory computing applications. A TiN/HfAlOx/Si-based ferroelectric memristor array exhibits intrinsic self-rectifying behavior, allowing selector-free crossbar integration. The device shows tunable short-term conductance decay characteristics, which are effectively exploited to implement physical reservoir computing (RC) for processing spatiotemporal signals. In parallel, a planar TiOx/Al₂O₃-based resistive memory array demonstrates analog switching with excellent linearity and endurance, enabling high-precision vector-matrix multiplication (VMM) operations with less than 2% error. To further enhance integration density, we developed a 3D vertical RRAM (VRRAM) array based on ALD deposited HfO₂ stacks, which provides reliable multi-level switching, high uniformity, and vertical scalability. The combination of 2D and 3D memory structures enables a compact, energy-efficient, and CMOS-compatible architecture for next-generation AI accelerators. These results underline the potential of hybrid memory arrays in realizing practical neuromorphic computing hardware with both learning and inference capabilities.
This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (RS-2024-00356939)
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