PHOTONIC INTEGRATED CIRCUIT FOR OPTICAL COMPUTING AND NEURAL NETWORKS

1 . A method of analyzing an image using an optical neural network (ONN), the method comprising:

transmitting, by an input photonic integrated circuits (PIC), a signal including a test pattern, wherein the test pattern represents an image to be analyzed;

filtering, by one or more convolution PIC layers, the signal including the test pattern, wherein the filtering is based on a target pattern;

comparing, by an analyzer PIC, the filtered signal to a threshold; and

outputting, by the analyzer PIC, results of the comparison.

2 . The method of claim 1 , further comprising:

performing, at a first lens, a Fourier Transform (FT) on the signal including the test pattern from the input PIC; and

performing, at a second lens, an inverse Fourier Transform (IFT) on the filtered signal from the one or more convolution PIC layers.

3 . The method of claim 1 , wherein outputting, by the analyzer PIC, the results of the comparison include saving the results in a memory of the ONN.

4 . The method of claim 1 , further comprising:

receiving, at the input PIC, the test pattern; and

encoding, at the input PIC, the test pattern on the signal.

5 . The method of claim 4 , wherein encoding, at the input PIC, the test pattern on the signal includes encoding components of the test pattern of the signal.

6 . The method of claim 1 , further comprising:

receiving, at one or more convolution PIC layers, the signal including the test pattern; and

receiving, at the one or more convolution PIC layers, a signal including the target pattern from a target PIC.

7 . The method of claim 6 , wherein:

receiving, at the one or more convolution PIC layers, the signal including the target pattern includes receiving a signal including a plurality of components of the target pattern, the plurality of components of the target pattern including a first component and a second component; and

filtering, by the one or more convolution PIC layers, the signal including the test pattern includes filtering the signal including the test pattern based on the first component at a first layer of the one or more convolution PIC layers and filtering the signal including the test pattern based on the second component at a second layer of the one or more convolution PIC layers.

8 . The method of claim 7 , wherein filtering the signal including the test pattern based on the first component at the first layer of the one or more convolution PIC layers and filtering the signal including the test pattern based on the second component at the second layer of the one or more convolution PIC layers are performed in parallel.

9 . The method of claim 7 , wherein filtering the signal including the test pattern based on the first component at the first layer of the one or more convolution PIC layers and filtering the signal including the test pattern based on the second component at the second layer of the one or more convolution PIC layers are performed sequentially.

10 . The method of claim 1 , wherein the threshold is a plurality of thresholds and each of the plurality of thresholds corresponds to a different component of the test pattern.

11 . The method of claim 1 , wherein the threshold is a confidence level threshold.

12 . The method of claim 1 , wherein the results of the comparison include a determination of a class of the test pattern or recognition of an image in the test pattern.

13 . An optical neural network (ONN) formed as a plurality of layers, the ONN comprising:

a first layer including an input photonic integrated circuit (PIC), the input PIC configured to transmit signals including one or more test patterns, wherein each test pattern represents an image to be analyzed;

a second layer including a first lens, the first lens configured to perform a Fourier Transform (FT) on signals passing therethrough;

a third layer including one or more convolution PIC layers, the one or more convolution PIC layers configured to filter received signals including one or more test patterns based on one or more target patterns;

a fourth layer including a second lens, the second lens configured to perform inverse Fourier Transform (IFT) on signals passing therethrough; and

a fifth layer including an analyzer PIC, the analyzer PIC configured to compare filtered signals from the third layer to one or more thresholds.

14 . The ONN of claim 13 , wherein the FT is Fast Fourier Transform (FFT) and the IFT is an inverse Fast Fourier Transform (IFFT).

15 . The ONN of claim 13 , wherein the first lens and the second lens are meta-lenses.

16 . The ONN of claim 13 , wherein the first lens and the second lens are stacked interlayered multiple planar diffractive cell layers.

17 . The ONN of claim 13 , wherein the comparison results in a determination of a class of a test pattern or recognition of an image in the test pattern.

18 . An optical neural network (ONN) formed as a plurality of layers, the ONN comprising:

a first layer including an input photonic integrated circuit (PIC), the input PIC configured to transmit signals beam including one or more test patterns, wherein each test pattern represents an image to be analyzed;

a second layer including one or more convolution PIC layers, the one or more convolution PIC layers configured to filter received signals including one or more test patterns based on one or more target patterns; and

a third layer including an analyzer PIC, the analyzer PIC configured to compare filtered optical beams from the second layer to a threshold.

19 . The ONN of claim 18 , wherein a first lens configured to perform a Fourier Transform (FT) on an optical beam passing therethrough is included in one of: i) the first layer, ii) the second layer, or ii) the third layer.

20 . The ONN of claim 18 , wherein a second lens, the second lens configured to perform inverse Fourier Transform (IFT) on an optical beam passing therethrough is included in one of: i) the first layer, ii) the second layer, or ii) the third layer.