ABSTRACT
Nonvolatile redox transistors (NVRTs) based upon Li-ion battery materials are demonstrated as memory elements for neuromorphic computer architectures with multi-level analog states, "write" linearity, low-voltage switching, and low power dissipation. Simulations of backpropagation using the device properties reach ideal classification accuracy. Physics-based simulations predict energy costs per "write" operation of <10 aJ when scaled to 200 nm × 200 nm.
ABSTRACT
The volumetric redox (chemical) capacitance of the surface of CeO2-δ films is quantified in situ to be 100-fold larger than the bulk values under catalytically relevant conditions. Sm addition slightly lowers the surface oxygen nonstoichiometry, but effects a 10-fold enhancement in surface chemical capacitance by mitigating defect interactions, highlighting the importance of differential nonstoichiometry for catalysis.