arXiv subjects

Folkert Horst

Publications and source records attributed to Folkert Horst.

3 recordsLinked to original sources

arXiv2026

Update Disturbance-Resilient Analog ReRAM Crossbar Arrays for In-Memory Deep Learning Accelerators

Resistive memory (ReRAM) technologies with crossbar array architectures hold significant potential for analog AI accelerator hardware, enabling both in-memory inference and training. Recent developments have successfully demonstrated inference acceleration by offloading compute-heavy training workloads to off-chip digital processors. However, in-memory acceleration of training algorithms is crucial for more sustainable and power-efficient AI, but still in an early stage of research. This study addresses in-memory training acceleration using analog ReRAM arrays, focusing on a key challenge during fully parallel weight updates: disturbances of the weight values in cross-point devices. A ReRAM device solution is presented on 350 nm silicon technology, utilizing a resistive switching conductive metal oxide (CMO) formed on a nanoscale conductive filament within a HfOx layer. The devices not only exhibit 60 ns fast, non-volatile analog switching, but also demonstrates outstanding resilience to update disturbances, enduring over 100k pulses. The disturbance tolerance of the ReRAM is analyzed using COMSOL Multiphysics simulations, modeling the filament-induced thermoelectric energy concentration that results in a highly nonlinear device responses to input voltage amplitudes. Disturbance-free parallel weight mapping is also demonstrated on the back-end-of-line integrated ReRAM array chip. Finally, comprehensive hardware-aware neural network simulations validate the potential of our ReRAM for in-memory deep learning accelerators capable of fully parallel weight updates.

cs.ET↗

arXiv2026

Study of Resistive Switching Dynamics and Memory States Equilibria in Analog Filamentary Conductive-Metal-Oxide/HfOx ReRAM via Compact Modeling

Resistive Random Access Memory (ReRAM) devices offer a promising solution for next-generation non-volatile memory and neuromorphic computing systems. Yet, existing compact models fail to capture analog resistive switching behavior of ReRAM devices. This work presents an advanced physics-based compact model for analog filamentary Conductive-Metal-Oxide (CMO)/HfOx ReRAM, capable of reproducing switching characteristics over a broad range of operating conditions. Compared to the state-of-the-art, the model extends the dynamic interplay between ion migration and electron hopping, while also accounting for parasitic resistive elements. Simulations of various voltage inputs are tested to reproduce quasi-static I-V curves, SET switching kinetics under single-pulse programming conditions, and analog accumulative conductance modulation upon bipolar identical pulse streams. Additional simulations reveal the physical criterion underlying the stabilization of the CMO/HfOx-based ReRAM memory state around the equilibrium point, namely symmetry point, under pulsing conditions when a fading memory mechanism emerges. Building upon the evidence of such equilibrium stabilization under pulsing and quasi-static conditions, a procedure is established to visualize and map equilibrium memory states across different input domains. The physical model supports design optimization of switching behavior for analog neuromorphic systems and non-volatile memory architectures. It also enables accurate integrated circuit simulations with CMO/HfOx-based ReRAM technology.

cs.ET↗

arXiv2025

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices

Analog in-memory computing is an emerging paradigm designed to efficiently accelerate deep neural network workloads. Recent advancements have focused on either inference or training acceleration. However, a unified analog in-memory technology platform-capable of on-chip training, weight retention, and long-term inference acceleration-has yet to be reported. This work presents an all-in-one analog AI accelerator, combining these capabilities to enable energy-efficient, continuously adaptable AI systems. The platform leverages an array of analog filamentary conductive-metal-oxide (CMO)/HfOx resistive switching memory cells (ReRAM) integrated into the back-end-of-line (BEOL). The array demonstrates reliable resistive switching with voltage amplitudes below 1.5V, compatible with advanced technology nodes. The array multi-bit capability (over 32 stable states) and low programming noise (down to 10nS) enable a nearly ideal weight transfer process, more than an order of magnitude better than other memristive technologies. Inference performance is validated through matrix-vector multiplication simulations on a 64x64 array, achieving a root-mean-square error improvement by a factor of 20 at 1 second and 3 at 10 years after programming, compared to state-of-the-art. Training accuracy closely matching the software equivalent is achieved across different datasets. The CMO/HfOx ReRAM technology lays the foundation for efficient analog systems accelerating both inference and training in deep neural networks.

cs.ET↗