The key to the study of flexible neuromorphic computing
is the
excellent weight update characteristic of neuromorphic devices. Electric-double-layer
transistors (EDLTs) include high transconductance, excellent stability
of threshold voltage, linear weight updates, and repetitive ion-concentration-dependent
switching properties. However, up to now, there is no report on a
flexible EDLT that provides all the aforementioned performance characteristics.
Here, a planar flexible floating-gate EDLT including an excellent
linear/symmetric weight update, a large number (>800) of conductance
states, repetitive switching endurance (>100 cycles), and low variation
in weight update is reported. After 800 signal stimulations, it is
found that the nonlinearity values of LTP are between 0.20 and 0.85,
those of LTD fall between 0.66 and 1.55, the symmetricity values are
between 120.7 and 639.8, and the dynamic range is between 150 and
352 nS. The study of 8 × 8 flexible floating-gate EDLT arrays
shows that the average deviation and standard deviation between the
experimental and theoretical values are 1.36 and 1.93, respectively,
indicating that the conductance regulation in the array has a relatively
small deviation. The different bending angles and the mechanical stability
of the floating-gate EDLT are also studied, which exhibit the excellent
bending properties. Furthermore, we studied the recognition of MNIST
handwritten digit images by a three-layer perceptron artificial neural
network with the experimental weight update, and the maximal recognition
accuracy is up to 87.8%.