Hybrid stochastic synapses enabled by scaled ferroelectric field-effect transistors

A. N.M.Nafiul Islam, Arnob Saha, Zhouhang Jiang, Kai Ni, Abhronil Sengupta

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Achieving brain-like density and performance in neuromorphic computers necessitates scaling down the size of nanodevices emulating neuro-synaptic functionalities. However, scaling nanodevices results in reduction of programming resolution and emergence of stochastic non-idealities. While prior work has mainly focused on binary transitions, in this work, we leverage the stochastic switching of a three-state ferroelectric field-effect transistor to implement a long-term and short-term two-tier stochastic synaptic memory with a single device. Experimental measurements are performed on a scaled 28 nm high-k metal gate technology-based device to develop a probabilistic model of the hybrid stochastic synapse. In addition to the advantage of ultra-low programming energies afforded by scaling, our hardware-algorithm co-design analysis reveals the efficacy of the two-tier memory in comparison to binary stochastic synapses in on-chip learning tasks - paving the way for algorithms exploiting multi-state devices with probabilistic transitions beyond deterministic ones.

Original languageEnglish (US)
Article number123701
JournalApplied Physics Letters
Issue number12
StatePublished - Mar 20 2023

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy (miscellaneous)

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