Nanosecond protonic programmable resistors for analog deep learning

• DOI: 10.1126/science.abp8064
• Publication Date: 2022-07-28T18:00:23Z
• Authors: Murat Onen; Nicolas Emond; Baoming Wang; Difei Zhang; Frances M. Ross; Ju Li; Bilge Yildiz; Jesús A. del Alamo
• Keywords: Nanosecond

Nanoscale ionic programmable resistors for analog deep learning are 1000 times smaller than biological cells, but it is not yet clear how much faster they can be relative to neurons and synapses. Scaling analyses of transport charge-transfer reaction rates point operation in the nonlinear regime, where extreme electric fields present within solid electrolyte its interfaces. In this work, we generated silicon-compatible nanoscale protonic with highly desirable characteristics under fields. This regime enabled controlled shuttling intercalation protons nanoseconds at room temperature an energy-efficient manner. The devices showed symmetric, linear, reversible modulation many conductance states covering a 20× dynamic range. Thus, space-time-energy performance all-solid-state artificial synapses greatly exceed that their counterparts.