Bulk switching RRAM technologies have been developed to address nonidealities of filamentary RRAM for embedded compute-in-memory applications. However, high density 3D integration and scalability to nano regime have yet to be experimentally demonstrated. Here, we present a scalable, filament-free 3D 8-layer vertical bulk RRAM (b-RRAM) technology optimized for embedded compute-in-memory (CIM) applications. This forming-free device features reliable cycling, multi-level switching, and enhanced speed via hydrogen doping. Guided by multiscale device simulations to optimize the switching stack, we demonstrate 40×40 nm2 b-RRAM cells with MΩ-level resistance and current nonlinearity, enabling accurate, energy-efficient matrix-vector multiplications (MVM) in selector-less crossbars. A hyperdimensional computing-based continual learning algorithm is implemented on 3D b-RRAM for edge AI tasks, achieving ~90% accuracy—comparable to high-precision floating-point (FP) baselines—while delivering substantial energy savings.
8-Layer Vertical Filament-Free Bulk RRAM with High Dynamic Range and Energy Efficiency for 3D Multilevel Compute-in-Memory / Zhou, Yucheng; Zhou, Yue; Kumar, Ashwani; Xu, Weihong; Song, Chang Eun; Palin, Victor; Padovani, Andrea; Thareja, Gaurav; Schuller, Ivan K.; Cauwenbergs, Gert; Rosing, Tajana; Kuzum, Duygu. - (2025), pp. 1-4. ( 2025 IEEE International Electron Devices Meeting (IEDM) San Francisco, CA, USA 06-10 December 2025) [10.1109/iedm50572.2025.11353838].
8-Layer Vertical Filament-Free Bulk RRAM with High Dynamic Range and Energy Efficiency for 3D Multilevel Compute-in-Memory
Padovani, Andrea;
2025
Abstract
Bulk switching RRAM technologies have been developed to address nonidealities of filamentary RRAM for embedded compute-in-memory applications. However, high density 3D integration and scalability to nano regime have yet to be experimentally demonstrated. Here, we present a scalable, filament-free 3D 8-layer vertical bulk RRAM (b-RRAM) technology optimized for embedded compute-in-memory (CIM) applications. This forming-free device features reliable cycling, multi-level switching, and enhanced speed via hydrogen doping. Guided by multiscale device simulations to optimize the switching stack, we demonstrate 40×40 nm2 b-RRAM cells with MΩ-level resistance and current nonlinearity, enabling accurate, energy-efficient matrix-vector multiplications (MVM) in selector-less crossbars. A hyperdimensional computing-based continual learning algorithm is implemented on 3D b-RRAM for edge AI tasks, achieving ~90% accuracy—comparable to high-precision floating-point (FP) baselines—while delivering substantial energy savings.| File | Dimensione | Formato | |
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(Y. Zhou - IEDM 2025) 8-Layer Vertical Filament-Free Bulk RRAM with High Dynamic Range and Energy Efficiency for 3D Multilevel Compute-in-Memory.pdf
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