A Thousand State Superlattice(SL) FEFET Analog Weight Cell

In-situ training of a deep neural network (DNN) requires high performance analog weight cell. In this work, we experimentally demonstrate a BEOL-compatible scaled-superlattice (FE-3nm/DE-4nm/FE-3nm) ferroelectric-FET analog weight cell with a maximum conductance level of 1,000 states, good linearity (α
p
/α
d
=-2/-1.58), excellent symmetry (α
p
~α
d
=0.42), fast-switching speed (100ns) and large dynamic range (G
max
/G
min
=80) using identical weight update pulses. System-level benchmarking shows that the SL FEFET analog weight cell can improve the online training accuracy of DNN to 91.05% with record energy consumption and latency. These results demonstrate the feasibilty of superlattice(SL) FEFET for high performance and energy efficient neuromorphic computing.