4. Experimental Section
Fabrication of ion-modulated memtransistor : The ion-modulated
memtransistors were fabricated on SiO2 substrates.
First, 5 nm Ti and 25 nm Au were deposited on the substrate through
electron-beam evaporation, in which electron-beam lithography was used
for patterning. After the source-drain electrode was formed by the
lift-off process, the second electron-beam lithography process was
carried out to pattern the channel region in the size of 10 um 10 um,
then 60 nm NbOx was deposited on the source-drain
contact by magnetron sputtering followed by the lift-off process to form
the channel. After that, 120 nm LiPON and 50 nm SiO2were prepared by magnetron sputtering as the electrolyte and passivation
layer followed by the third electron-beam lithography process. After
sputtering was done, the lift-off process was utilized to remove excess
material. Finally, after the fourth electron-beam lithography process,
10 nm Ti and 220 nm Au were deposited using electron-beam evaporation.
After the last lift-off process, the pads for all the terminals of this
device were all completed.
Material characterization : To analyze the material component of
the ion-modulated transistors, the TEM samples were prepared firstly by
the focus ion beam (FIB) technique (Helios G5 UX). After that, TEM, as
well as EDS tests were performed on Talos F200X G2 systems.
Electrical Measurements : All the electrical measurements were
performed using an Agilent B1500A semiconductor parameter analyzer. In
the pulse testing part, the pulse signal was applied on the gate
electrode while the drain-source channel was always under 0.2 V bias.
Simulations : To demonstrate the neuromorphic artificial vision
systems based on ion-modulated memtransistors, we adopt the multilayer
perceptrons model with the structure of 784-400-200-10, and the
Fashion-Mnist was chosen as the testing dataset, which includes ten
classes of daily outfits. All the simulations used the backpropagation
algorithm to update the connected weights in the neural network. In the
simulation of the hardware-softplus neuron function, we take them into
the hidden layer and the output layer, the activation of which was
applied a softmax followed by a logarithm. Besides, in the simulation of
the filtering unit, we added the softplus-like function before the input
layer, with other structures unchanged.