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20112023
most citedHafnia-based Double Layer Ferroelectric Tunnel Junctions as Artificial Synapses for Neuromorphic Computing

151 citations · 705 across the 22 of their papers we have counts for

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physics.app-ph2023★ 12 cited

A multi-pulse wakeup scheme for on-chip operation of devices based on ferroelectric doped HfO2 thin films

Suzanne Lancaster, Thomas Mikolajick, Stefan Slesazeck

A wakeup scheme for ferroelectric thin HfZrO films is presented, based on a gradual switching approach using multiple short pulses with a voltage amplitude roug…

physics.app-ph2022★ 10 cited

Improvement of FTJ on-current by work function engineering for massive parallel neuromorphic computing

Suzanne Lancaster, Quang T. Duong, Erika Covi +2

HfO2-based ferroelectric tunnel junctions (FTJs) exhibit attractive properties for adoption in neuromorphic applications. The combination of ultra-low-power multi-level switching c…

physics.app-ph2021★ 151 cited

Hafnia-based Double Layer Ferroelectric Tunnel Junctions as Artificial Synapses for Neuromorphic Computing

Benjamin Max, Michael Hoffmann, Halid Mulaosmanovic +2

Ferroelectric tunnel junctions (FTJ) based on hafnium zirconium oxide (Hf1-xZrxO2; HZO) are a promising candidate for future applications, such as low-power memories and neuromorph…

physics.app-ph2021★ 21 cited

On the Performance of Dual-Gate Reconfigurable Nanowire Transistors

Bin Sun, Benjamin Richstein, Patrick Liebisch +5

We investigate the operation of dual-gate reconfigurable field-effect transistor (RFET) in the programgate at drain (PGAD) and program-gate at source (PGAS) configurations. To this…

physics.app-ph2020★ 61 cited

Top-down fabricated reconfigurable FET with two symmetric and high-current on-states

Maik Simon, Boshen Liang, Dustin Fischer +4

We demonstrate a top-down fabricated reconfigurable field effect transistor (RFET) based on a silicon nanowire that can be electrostatically programmed to p- and n-configuration. T…