IP Library › Granted Patent US 12,731,022
Granted Patent B2
US 12,731,022 · App. 17/920,778 · Granted Sep 8, 2026

Misalignment-resilient diffractive optical neural networks

Inventors: Aydogan Ozcan (Los Angeles, CA); Deniz Mengu (Los Angeles, CA); Yair Rivenson (Los Angeles, CA)
Assignee: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
G06N3/067G06N3/0675
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Quick Facts
Patent No.
US 12,731,022
App. No.
17/920,778
Granted
Sep 8, 2026
Kind
B2
Abstract

A diffractive optical neural network includes one more layers that are resilient to misalignments, fabrication-related errors, detector noise, and/or other sources of error. A diffractive optical neural network model is first trained with a computing device to perform a statistical inference task such as image classification (e.g., object classification). The model is trained using images or training optical signals along with random misalignments of the plurality of layers, fabrication-related errors, input plane or output plane misalignments, and/or detector noise, followed by computing an optical output of the diffractive optical neural network model through optical transmission and/or reflection resulting from the diffractive optical neural network and iteratively adjusting complex-valued transmission and/or reflection coefficients for each layer until optimized transmission/reflection coefficients are obtained. Once the model is optimized, the physical embodiment of the diffractive optical neural network is manufactured.

Claims (39)

1 . A method of forming a diffractive optical neural network comprising one or more layers that are resilient to misalignments, fabrication-related errors, detector noise, and/or other sources of error comprising:

training with a computing device a diffractive optical neural network model to perform one or more specific optical functions for a transmissive and/or reflective optical neural network having one or more optically transmissive and/or optically reflective physical features located in different two dimensional locations in each of the one or more layers, wherein the training comprises feeding an input plane of the diffractive optical neural network model with training images or training optical signals along with random misalignments of the one or more diffractive layers, fabrication-related errors, input plane or output plane misalignments, and/or detector noise, followed by computing an optical output of the diffractive optical neural network model through optical transmission and/or reflection resulting from the optical neural network and iteratively adjusting complex-valued transmission and/or reflection coefficients for each layer until optimized transmission/reflection coefficients are obtained; and

manufacturing or having manufactured a physical embodiment of the diffractive optical neural network comprising at least one transmissive and/or reflective layers having physical features that match the optimized transmission/reflection coefficients obtained by the training of diffractive optical neural network model.

2 . The method of claim 1 , wherein the random misalignments comprise displacement vectors in an x, y, and/or z directions for one or more of the layers and/or displacement vectors in the input plane and/or output plane, wherein x, y, and z are directions in three-dimensional space.

3 . The method of claim 1 , wherein one or more of the layers comprise reconfigurable spatial light modulators.

4 . The method of claim 1 , wherein the random misalignment comprises in-plane rotation of one or more of the layers, input plane, and/or output plane.

5 . The method of claim 1 , wherein the fabrication-related errors comprise additive printing or 3D fabrication errors.

6 . The method of claim 1 , wherein the physical embodiment of the diffractive optical neural network has an inference performance that is within a range of the inference performance of an equivalent diffractive optical neural network that does not have any misalignments, fabrication-related errors, and/or other sources of error taken into account during training.

7 . The method of claim 1 , wherein the physical diffractive optical neural network outputs an optical image and/or optical signal to one or more optical detectors.

8 . The method of claim 7 , wherein the one or more optical detectors comprise one or more optical detectors specifically assigned to a particular object, image, or data class.

9 . The method of claim 7 , the one or more optical detectors output signal(s) or data to a trained electronic neural network.

10 . The method of claim 1 , wherein the physical diffractive optical neural network outputs an optical image or optical signal to a plurality of groups of optical detectors configured to sense the output optical images or output optical signals, wherein each group of optical detectors comprises at least one optical detector configured to capture a virtually positive signal from the output optical images or output optical signals and at least one optical detector configured to capture a virtually negative signal from the output optical images or output optical signals.

11 . The method of claim 10 , wherein the virtually positive and virtually negative signals are used to calculate a differential signal for an input optical image or signal input to the physical diffractive optical neural network to perform an inference and/or classification task.

12 . A diffractive optical neural network comprising one or more layers that are resilient to misalignments, fabrication-related errors, detector noise, and/or other sources of error comprising:

one or more optically transmissive layers arranged in an optical path, each of the one or more optically transmissive layers comprising a plurality of physical features formed on or within the one or more optically transmissive layers and having different complex-valued transmission coefficients as a function of lateral coordinates across each layer, wherein the one or more optically transmissive layers and the plurality of physical features thereon collectively define a trained mapping function between an input optical image or input optical signal to the one or more optically transmissive layers and an output optical image or output optical signal created by optical diffraction through the one or more optically transmissive layers, the trained mapping function being resilient to one or more of: misalignment of one or more of the optically transmissive layers, misalignment of an input plane, misalignment of an output plane, fabrication-related errors in the optically transmissive layers and/or in the diffractive network, detector noise, and/or other sources of error; and

one or more optical detectors configured to capture the output optical image or output optical signal resulting from the one or more optically transmissive layers.

13 . The diffractive optical neural network of claim 12 , wherein one or more of the layers comprise reconfigurable spatial light modulators.

14 . The diffractive optical neural network of claim 12 , wherein the misalignment(s) and/or fabrication-related errors comprise displacement vectors in an x, y, and/or z directions for one or more of the layers and/or displacement vectors in the input plane and/or the output plane, wherein x, y, and z are directions in three-dimensional space.

15 . The diffractive optical neural network of claim 12 , wherein the misalignment(s) comprises in-plane rotation of one or more of the layers, the input plane, and/or the output plane.

16 . The diffractive optical neural network of claim 12 , wherein the fabrication-related errors comprise additive printing or 3D fabrication errors.

17 . The diffractive optical neural network of claim 12 , wherein the diffractive optical neural network has an inference performance that is within a range of the inference performance of an equivalent diffractive optical neural network that does not have any misalignments, fabrication-related errors, and/or other sources of error taken into account during training.

18 . The diffractive optical neural network of claim 12 , wherein the diffractive optical neural network outputs an optical image and/or optical signal to one or more optical detectors.

19 . The diffractive optical neural network of claim 18 , wherein the one or more optical detectors comprise one or more optical detectors specifically assigned to a particular object, image, or data class.

20 . The diffractive optical neural network of claim 18 , further comprising a trained electronic neural network, wherein the one or more optical detectors output signal(s) or data to the trained electronic neural network.

21 . The diffractive optical neural network of claim 12 , wherein the diffractive optical neural network outputs an optical image or optical signal to a plurality of groups of optical detectors configured to sense the output optical images or output optical signals, wherein each group of optical detectors comprises at least one optical detector configured to capture a virtually positive signal from the output optical images or output optical signals and at least one optical detector configured to capture a virtually negative signal from the output optical images or output optical signals.

22 . The diffractive optical neural network of claim 12 , wherein the virtually positive and virtually negative signals are used to calculate a differential signal for an input optical image or signal input to the diffractive optical neural network to perform an inference and/or classification task.

23 . A diffractive optical neural network comprising one or more layers that are resilient to misalignments, fabrication-related errors, detector noise, and/or other sources of error comprising:

one or more optically reflective layers arranged along an optical path, each of the one or more optically reflective layers comprising a plurality of physical features formed on or within the one or more optically reflective layers, wherein the one or more optically reflective layers and the plurality of physical features collectively define a trained mapping function between an input optical image or input optical signal to the one or more optically reflective layers and an output optical image or output optical signal from the one or more optically reflective layers, the trained mapping function being resilient to one or more of: misalignment of one or more of the optically reflective layers, misalignment of an input plane, misalignment of an output plane, fabrication-related errors in the optically reflective layers and/or the diffractive network, detector noise, and/or other sources of error; and

one or more optical detectors configured to capture the output optical image or output optical signal from the one or more optically reflective layers.

24 . The diffractive optical neural network of claim 23 , wherein one or more of the layers comprise reconfigurable spatial light modulators.

25 . The diffractive optical neural network of claim 23 , wherein the misalignment(s) and/or fabrication-related errors comprise displacement vectors in an x, y, and/or z directions for the one or more layers and/or displacement vectors in the input plane and/or the output plane, wherein x, y, and z are directions in three-dimensional space.

26 . The diffractive optical neural network of claim 23 , wherein the misalignment(s) comprises in-plane rotation of the one or more layers, the input plane, and/or the output plane.

27 . The diffractive optical neural network of claim 23 , wherein the fabrication-related errors comprise additive printing or 3D fabrication errors.

28 . The diffractive optical neural network of claim 23 , wherein the diffractive optical neural network has an inference performance that is within a range of the inference performance of an equivalent diffractive optical neural network that does not have any misalignments, fabrication-related errors, and/or other sources of error taken into account during training.

29 . The diffractive optical neural network of claim 23 , wherein the diffractive optical neural network outputs an optical image and/or optical signal to one or more optical detectors.

30 . The diffractive optical neural network of claim 29 , wherein the one or more optical detectors comprise one or more optical detectors specifically assigned to a particular object, image, or data class.

31 . The diffractive optical neural network of claim 29 , further comprising a trained electronic neural network, wherein the one or more optical detectors output signal(s) or data to the trained electronic neural network.

32 . The diffractive optical neural network of claim 23 , wherein the diffractive optical neural network outputs an optical image or optical signal to a plurality of groups of optical detectors configured to sense the output optical images or output optical signals, wherein each group of optical detectors comprises at least one optical detector configured to capture a virtually positive signal from the output optical images or output optical signals and at least one optical detector configured to capture a virtually negative signal from the output optical images or output optical signals.

33 . The diffractive optical neural network of claim 23 , wherein the virtually positive and virtually negative signals are used to calculate a differential signal for an input optical image or signal input to the diffractive optical neural network to perform an inference and/or classification task.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 22, 2022
From: OZCAN, AYDOGAN; MENGU, DENIZ; RIVENSON, YAIR
To: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
Reel/Frame 061504/0424 →
Continuity (2)
Provisional Application 63029268 · May 22, 2020
Related Publication 20230162016A1 · May 25, 2023
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