112 citations · 208 across the 9 of their papers we have counts for
9 papers
Energy-convergence trade off for the training of neural networks on bio-inspired hardware
Nikhil Garg, Paul Uriarte Vicandi, Yanming Zhang +5
The increasing deployment of wearable sensors and implantable devices is shifting AI processing demands to the extreme edge, necessitating ultra-low power for continuous operation.…
All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi +9
Analog in-memory computing is an emerging paradigm designed to efficiently accelerate deep neural network workloads. Recent advancements have focused on either inference or trainin…
TEXEL: A neuromorphic processor with on-chip learning for beyond-CMOS device integration
Hugh Greatorex, Ole Richter, Michele Mastella +10
Recent advances in memory technologies, devices and materials have shown great potential for integration into neuromorphic electronic systems. However, a significant gap remains be…
High-Conductance, Ohmic-like HfZrO Ferroelectric Memristor
Laura Bégon-Lours, Mattia Halter, Youri Popoff +3
The persistent and switchable polarization of ferroelectric materials based on HfO-based ferroelectric compounds, compatible with large-scale integration, are attractive synapt…
A BEOL Compatible, 2-Terminals, Ferroelectric Analog Non-Volatile Memory
Laura Bégon-Lours, Mattia Halter, Diana Dávila Pineda +6
A Ferroelectric Analog Non-Volatile Memory based on a WOx electrode and ferroelectric HfZrO layer is fabricated at a low thermal budget (~375C), enabling BEOL processes…
A Back-End-Of-Line Compatible, Ferroelectric Analog Non-Volatile Memory
Laura Bégon-Lours, Mattia Halter, Diana Dávila Pineda +6
A Ferroelectric Analog Non-Volatile Memory based on a WOx electrode and ferroelectric HfZrO4 layer is fabricated at a low thermal budget (~375C), enabling BEOL processes and CMOS i…
A back-end, CMOS compatible ferroelectric Field Effect Transistor for synaptic weights
Mattia Halter, Laura Bégon-Lours, Valeria Bragaglia +5
Neuromorphic computing architectures enable the dense co-location of memory and processing elements within a single circuit. This co-location removes the communication bottleneck o…
Stabilization of phase-pure rhombohedral HfZrO4 in Pulsed Laser Deposited thin films
Laura Bégon-Lours, Martijn Mulder, Pavan Nukala +6
Controlling the crystalline structure of Hafnium Zirconate and its epitaxial relationship to a semiconducting electrode has a high technological interest, as ferroelectric material…
Factors limiting ferroelectric field-effect doping in complex-oxide heterostructures
L. Bégon-Lours, V. Rouco, Qiao Qiao +12
Ferroelectric field-effect doping has emerged as a powerful approach to manipulate the ground state of correlated oxides, opening the door to a new class of field-effect devices. H…