Synaptic device, reservoir computing device including the synaptic device, and reservoir computing method using the computing device

1 . A reservoir computing device comprising:

an input signal generation unit configured to generate an input signal corresponding to a learning target pattern;

a measurement unit configured to measure a result according to the input signal; and

a learning unit configured to learn the learning target pattern through values measured by the measurement unit,

wherein the measurement unit comprises:

a substrate; and

a plurality of unit cells on the substrate,

wherein the unit cells each comprise a channel layer and a first electrode and second electrode intersecting the channel layer,

wherein the first electrode and the second electrode are spaced apart from each other, and define a gap region exposing a portion of the channel layer, and

the channel layer comprises a 2-dimensional semiconductor material or a 2-dimensional ferroelectric material.

2 . The reservoir computing device of claim 1 , wherein the learning target pattern is a consonant, vowel, syllable, word, sentence, nonverbal symbol, picture, or figure.

3 . The reservoir computing device of claim 1 , wherein the input signal is a signal that changes over time.

4 . The reservoir computing device of claim 3 , wherein the input signal includes at least one of an electric pulse signal or an optical pulse signal.

5 . The reservoir computing device of claim 1 , wherein the result according to the input signal, measured by the measurement unit, is an electric conductivity value of the channel layer, which changes according to the input signal.

6 . The reservoir computing device of claim 1 , wherein the learning target pattern is expressed by a plurality of rows, and each of the plurality of rows is expressed by a plurality of input signals.

7 . The reservoir computing device of claim 1 , wherein the learning unit uses single-layer perceptron, multi-layer perceptron, random forest, support vector machine, or logistic regression.

8 . A reservoir computing method comprising:

preparing a learning target pattern;

representing the learning target pattern by pulse signals;

inputting each of the pulse signals to a memristor; and

training with the learning target pattern through conductivity values of the memristor,

wherein the memristor comprises a channel layer and a first electrode and second electrode intersecting the channel layer,

wherein the first electrode and the second electrode are spaced apart from each other, and define a gap region exposing a portion of the channel layer, and

the channel layer comprises a 2-dimensional semiconductor material or a 2-dimensional ferroelectric material.

9 . The reservoir computing method of claim 8 , wherein each of the pulse signals is a binary pulse signal.

10 . The reservoir computing method of claim 8 , wherein the representing the learning target pattern by the pulse signals comprises:

representing the learning target pattern by a plurality of rows; and

representing each of the plurality of rows by the pulse signals.

11 . The reservoir computing method of claim 8 , wherein the training with the learning target pattern through the conductivity values of the memristor comprises:

extracting the conductivity values from a conductivity graph of the memristor; and

inputting the conductivity values to a machine learning model.

12 . The reservoir computing method of claim 8 ,

wherein the pulse signals include at least one of an electric pulse signal or an optical pulse signal,

wherein the electric pulse signal is input through the first electrode or the second electrode, and

the optical pulse signal is input through the gap region.