# US 12,761,463 B2 · Systems and methods for tuning optical cavities using machine learning techniques

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- Number: US 12,761,463 B2
- Document ID: US12761463B2
- Status: Granted
- Kind: B2
- Application number: 18/021,986
- Filed: 2021-08-18
- Published: 2023-09-28
- Granted: 2026-10-13
- Earliest claimed: 2020-08-18
- Assignees: Qunnect, Inc.
- Applicants: Qunnect, Inc.
- Inventors: Mehdi Namazi; Mael Flament; Rourke Sekelsky; Michelle Fritz; Gabriel Bello Portmann
- Web page: https://patentgrep.com/patent/US12761463B2

## CPC

- G02B 26/001: {based on interference in an adjustable optical cavity (interference filters G02B5/28; devices or arrangements using multiple reflections in spectrometry or monochromators G01J3/26)}
- G06N 3/092: Reinforcement learning
- G02B: OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

An optical system including an optical cavity and a method of tuning an optical cavity using a machine learning model is provided. The method includes determining a tuning parameter of the optical cavity by: analyzing, using a convolutional neural network (CNN) model, a measurement signal obtained from the optical cavity to determine a degree of misalignment of the optical cavity; and determining, using a reinforcement learning (RL) model, the tuning parameter based on the degree of misalignment of the optical cavity.

## Claims

### Claim 1

A method of tuning an optical cavity, the method comprising:
determining a tuning parameter of the optical cavity, wherein determining the tuning parameter comprises:
analyzing, using a convolutional neural network (CNN) model, a measurement signal obtained from the optical cavity to determine a degree of misalignment, wherein determining the degree of misalignment comprises using the CNN model to determine a difference between the measurement signal and a spatial profile image comprising a Gaussian zero-order mode; and
determining, using a reinforcement learning (RL) model, the tuning parameter based on the degree of misalignment; and
tuning the optical cavity using the tuning parameter.

### Claim 2 (depends on 1)

The method of claim 1, further comprising determining, using a machine learning model, when to determine the tuning parameter of the optical cavity based on a threshold transmission value.

### Claim 3 (depends on 1)

The method of claim 1, further comprising determining when to determine the tuning parameter of the optical cavity based on a temperature measurement of the optical cavity and/or an environment of the optical cavity.

### Claim 4 (depends on 1)

The method of claim 1, wherein tuning the optical cavity using the tuning parameter comprises changing a spacing between cavity walls of the optical cavity based on the tuning parameter.

### Claim 5 (depends on 4)

The method of claim 4, wherein changing the spacing between the cavity walls of the optical cavity comprises changing a temperature of the optical cavity and/or changing the spacing between the cavity walls using piezoelectric actuators.

### Claim 6 (depends on 1)

The method of claim 1, wherein tuning the optical cavity using the tuning parameter comprises changing a reflectivity of one or more mirrors of the optical cavity based on the tuning parameter by changing a temperature of the optical cavity.

### Claim 7 (depends on 1)

The method of claim 1, wherein analyzing the measurement signal comprises analyzing a measurement of light exiting the optical cavity.

### Claim 8 (depends on 7)

The method of claim 7, further comprising capturing the measurement of light using a two-dimensional detector array disposed in a plane perpendicular to a direction of the light exiting the optical cavity, wherein capturing the measurement of light comprises capturing a spatial profile of the light exiting the optical cavity.

### Claim 9 (depends on 8)

The method of claim 8, wherein capturing a spatial profile of the light exiting the optical cavity comprises capturing information characterizing a transverse-spatial mode of the optical cavity.

### Claim 10 (depends on 8)

The method of claim 8, wherein capturing the measurement of light comprises capturing an intensity and/or a power spectrum of the light.

### Claim 11 (depends on 10)

The method of claim 10, further comprising periodically obtaining the measurement signal from the optical cavity, classifying the measurement signal using the CNN model, determining the tuning parameter of the optical cavity using the RL model, and tuning the optical cavity.

### Claim 12

An optical system, comprising:
an optical cavity;
at least one processor coupled to the optical cavity; and
at least one computer-readable storage medium storing computer-executable instructions that, when executed by the at least one processor, cause the at least one processor to carry out a method comprising:
analyzing, using a convolutional neural network (CNN) model, a measurement signal obtained from the optical cavity to determine a degree of misalignment, wherein determining the degree of misalignment comprises using the CNN model to determine a difference between the measurement signal and a spatial profile image comprising a Gaussian zero-order mode;
determining, using a reinforcement learning (RL) model, a tuning parameter based on the degree of misalignment; and
tuning the optical cavity using the tuning parameter.

### Claim 13 (depends on 12)

The optical system of claim 12, wherein determining the tuning parameter comprises generating the tuning parameter using the RL model, the tuning parameter being based on the difference between the measurement signal and the spatial profile image determined by the CNN model.

### Claim 14 (depends on 12)

The optical system of claim 12, wherein the optical cavity comprises a high finesse optical cavity.

### Claim 15 (depends on 14)

The optical system of claim 14, wherein the high finesse optical cavity comprises an optical cavity comprising a finesse value greater than or equal to 100 and less than or equal to 20,000.

### Claim 16 (depends on 15)

The optical system of claim 15, wherein the high finesse optical cavity comprises a Fabry-Perot etalon.

### Claim 17 (depends on 12)

The optical system of claim 12, wherein the optical cavity comprises a cavity wall comprising a surface that is flat, concave, convex, and/or a combination thereof.

### Claim 18 (depends on 17)

The optical system of claim 17, wherein the surface comprises a reflective coating.

### Claim 19 (depends on 12)

The optical system of claim 12, wherein the measurement signal is an image of a spatial profile of light exiting the optical cavity, the image characterizing a transverse spatial mode of the optical cavity.

### Claim 20

At least one computer-readable storage medium encoded with computer-executable instructions that, when executed by a computer, cause the computer to carry out a method comprising:
analyzing, using a convolutional neural network (CNN) model, a measurement signal obtained from an optical cavity to determine a degree of misalignment, wherein determining the degree of misalignment comprises using the CNN model to determine a difference between the measurement signal and a spatial profile image comprising a Gaussian zero-order mode;
determining, using a reinforcement learning (RL) model, a tuning parameter based on the degree of misalignment; and
tuning the optical cavity using the tuning parameter.