Integrated optical 90-degree optical mixer based on optical neural network

Abstract

The invention provides an integrated optical 90-degree optical mixer based on an optical neural network, which comprises an optical neural network chip configured with an optimal transmission matrix and used for processing received coherent light, generating analog signal light and reference light and mixing the analog signal light and the reference light to form four paths of light with different phases; wherein the initial optical neural network chip is trained by adopting a gradient descent algorithm so as to obtain the optical neural network chip configured with the optimal transmission matrix. According to the invention, the transmission matrix of the optical neural network chip fits the transmission matrix of the 90-degree optical mixer through the gradient descent algorithm, so that the task of the 90-degree optical mixer is realized. According to the optical 90-degree optical mixer, due to trainability, the optical 90-degree optical mixer does not depend on complex structure design and low-error processing means any more, has better robustness, and can compensate for the coupling unbalance degree output by different optical paths.

Description

Technical Field

[0001] The present application relates to the field of photon integration technology, and in particular to an integrated optical 90-degree optical mixer based on an optical neural network. Background Art

[0002] 90° optical mixers (90° Hybrid) have important applications in many fields such as coherent communication and coherent detection based on I / Q demodulation. During the demodulation process, the 90° optical mixer at the receiving end mixes the received signal light with the reference light and generates two sets of orthogonal signal components. The mixed signal is demodulated to obtain the amplitude and phase information carried in the signal light. Current 90° optical mixer implementations are all passive devices, such as 2×4 multi-mode interferometers (Multiple-Mode Interference, MMI), hybrid multi-mode interferometers, etc.

[0003] However, due to the inevitable errors caused by simulation during processing and design, the degradation of performance parameters such as phase error and imbalance after the actual 90° optical mixer mixes the signal light and the reference light will lead to distortion of the demodulated signal, thereby significantly increasing the complexity of subsequent digital signal processing. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, the purpose of the present application is to provide an integrated optical 90-degree optical mixer based on an optical neural network, so as to solve the problem of demodulation signal distortion caused by the degradation of performance parameters of the existing 90-degree optical mixer.

[0005] To achieve the above-mentioned purpose and other related purposes, the first aspect of the present application provides an integrated optical 90-degree optical mixer based on an optical neural network, comprising: an optical neural network chip configured with an optimal transmission matrix, used to process the received coherent light, generate simulated signal light and reference light, and mix the simulated signal light and reference light to form four paths of light with different phases; wherein, a gradient descent algorithm is used to train the initial optical neural network chip to obtain an optical neural network chip configured with an optimal transmission matrix.

[0006] In some embodiments of the first aspect of the present application, the optical neural network chip includes a beam splitting structure and a multimode interference cascade network; wherein the beam splitting structure is used to generate simulated signal light and reference light; and the multimode interference cascade network is used to mix the simulated signal light and reference light.

[0007] In some embodiments of the first aspect of the present application, the beam splitting structure includes a first multimode interferometer; the first multimode interferometer is connected to two first output waveguides, and one of the first output waveguides is equipped with a first modulator; wherein, one light path output by the first multimode interferometer is controlled by the first modulator to form a simulated signal light, and the other light path is used as a reference light.

[0008] In some embodiments of the first aspect of the present application, the width of the first multi-mode interferometer is 8 microns and the length is 58.248 microns.

[0009] In some embodiments of the first aspect of the present application, the multi-mode interference cascade network is provided with a plurality of forward propagation training layers in sequence along the propagation direction of light.

[0010] In some embodiments of the first aspect of the present application, each forward propagation training layer is composed of a second multimode interferometer and four second modulators; wherein the second multimode interferometer is connected to four second output waveguides, and each second output waveguide is equipped with a second modulator.

[0011] In some embodiments of the first aspect of the present application, the gradient descent algorithm is used to train the initial optical neural network chip to obtain an optical neural network chip configured with an optimal transmission matrix. The specific process includes: obtaining the phase loaded by each second modulator in the initial multi-mode interference cascade network to obtain an initial transmission matrix; based on the initial transmission matrix, using the gradient descent algorithm to obtain the optimal transmission matrix; according to the optimal transmission matrix, the phase of each second modulator in the initial multi-mode interference cascade network is controlled to obtain an optical neural network chip configured with an optimal transmission matrix.

[0012] In some embodiments of the first aspect of the present application, each second multi-mode interferometer has a width of 16 microns and a length of 466.52 microns.

[0013] In some embodiments of the first aspect of the present application, the tapered length of each port of each second multi-mode interferometer is 30 microns and the width is 3 microns.

[0014] In some embodiments of the first aspect of the present application, each second modulator has a length of 250 microns and a width of 3 microns.

[0015] As described above, the integrated optical 90-degree optical mixer based on optical neural network of the present application has the following beneficial effects:

[0016] The present application uses a gradient descent algorithm to fit the transmission matrix of the optical neural network chip to the transmission matrix of the 90-degree optical mixer to achieve the 90-degree optical mixer task. The optical 90-degree optical mixer of the present application is trainable and no longer relies on complex structural design and low-error processing methods. It has better robustness and can compensate for the imbalance of output coupling of different optical paths. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Shown is a schematic structural diagram of an integrated optical 90-degree optical mixer based on an optical neural network in one embodiment of the present application.

[0018] Figure 2 Shown is a schematic structural diagram of a first multi-mode interferometer in a specific embodiment of the present application.

[0019] Figure 3 Shown is a schematic diagram of a forward propagation training layer in one embodiment of the present application.

[0020] Figure 4 Shown is a schematic diagram of a forward propagation training layer in another embodiment of the present application.

[0021] Figure 5 Shown is a schematic diagram comparing the expected results of the output intensity change with the input phase of the optical neural network chip after training at a wavelength of 1550nm in a specific embodiment of the present application.

[0022] Figure 6 Shown is a schematic diagram of the phase error in the wavelength range of 1530nm to 1570nm output of an optical neural network chip after training at a wavelength of 1550nm in a specific embodiment of the present application.

[0023] Figure 7 Shown is a schematic diagram of the common mode rejection ratio of an optical neural network chip in a specific embodiment of the present application, which is output in the wavelength range of 1530nm to 1570nm after being trained at a wavelength of 1550nm. DETAILED DESCRIPTION

[0024] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0025] In the embodiments of the present application, words such as "first" and "second" are used to distinguish the same or similar items with substantially the same functions and effects. For example, the first multimode interferometer and the second multimode interferometer are only used to distinguish different multimode interferometers, and do not limit their order. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit them to be different.

[0026] It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" represent examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0027] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc or abc, where a, b, c can be single or multiple.

[0028] To facilitate understanding of the embodiments of the present application, first Figure 1 Detailed description. Figure 1 A schematic block diagram of an integrated optical 90-degree optical mixer based on an optical neural network in an embodiment of the present invention is shown. The integrated optical 90-degree optical mixer based on an optical neural network in this embodiment includes:

[0029] An optical neural network chip configured with an optimal transmission matrix is ​​used to process received coherent light to generate simulated signal light and reference light, and mix the simulated signal light and reference light to form four paths of light with different phases;

[0030] Among them, a gradient descent algorithm is used to train the initial optical neural network chip to obtain an optical neural network chip configured with an optimal transmission matrix.

[0031] It should be noted that on-chip optical neural networks (ONNs) are an application form of integrated optics. It is a new computing architecture that uses a variety of optical devices (such as optical waveguides, beam splitters, phase modulators, and optical resonant rings) to implement neural network computing. By encoding information into physical properties such as intensity, phase, or polarization of light, this technology can complete the core matrix computing tasks in neural networks in a high-speed parallel and low-power manner.

[0032] Integrated photonics technology can integrate high-density, compact optical links onto a single chip, and due to its high compatibility with complementary metal oxide semiconductor (CMOS) processes, it not only greatly reduces manufacturing costs, but also significantly improves production efficiency and reliability. Integrated photonics has broad application potential in future data centers, high-speed interconnection systems, optical communications, and quantum computing.

[0033] In one embodiment, the four paths of light with different phases have relative phases of 0 degree, 90 degree, 180 degree and 270 degree respectively.

[0034] In one embodiment, the optical neural network chip includes a silicon substrate, a silicon dioxide lower cladding layer, a silicon device layer, a silicon dioxide upper cladding layer, a modulator layer and an aluminum wire layer arranged in sequence from bottom to top.

[0035] In one embodiment, the optical neural network chip includes an input grating, a beam splitting structure, a multi-mode interference cascade network, and an output grating.

[0036] In one embodiment, if Figure 1 As shown, the beam splitting structure includes a first multimode interferometer 1; the first multimode interferometer 1 is connected to two first output waveguides 2, and one of the first output waveguides 2 is equipped with a first modulator 3; wherein, one path of light output by the first multimode interferometer 1 is controlled by the first modulator 3 to form a simulated signal light, and the other path of light is used as a reference light.

[0037] In a specific embodiment, the first multi-mode interferometer 1 is a 1×2 multi-mode interferometer (Multiple-Mode Interference, MMI), and the 1×2 multi-mode interferometer has one input port and two output ports. Preferably, in order to reduce insertion loss, Figure 2 As shown, the width W_splitter of the first multimode interferometer 1 is 8 um, the length L_splitter of the first multimode interferometer 1 is 58.248 um, and the insertion loss is 0.046 dB after simulation.

[0038] In one embodiment, if Figure 1As shown, two output ports of the first multimode interferometer 1 are respectively connected to a first output waveguide 2 .

[0039] In one embodiment, the first multimode interferometer 1 and the two first output waveguides 2 are disposed in the silicon device layer, and the first modulator 3 is disposed in the modulator layer.

[0040] In one embodiment, the first modulator may be a thermo-optic modulator or an electro-optic modulator, which is not limited in the present invention.

[0041] In one embodiment, if Figure 3 as well as Figure 4 As shown, the multimode interference cascade network is provided with a plurality of forward propagation training layers in sequence along the propagation direction of light. Specifically, the number of the forward propagation training layers is generally 4, namely forward propagation training layer A, forward propagation training layer B, forward propagation training layer C and forward propagation training layer D.

[0042] In one embodiment, if Figure 1 , Figure 3 as well as Figure 4 As shown, each forward propagation training layer is composed of a second multimode interferometer 4 and four second modulators 5 ; wherein the second multimode interferometer 4 is connected to four second output waveguides 7 , and each second output waveguide 7 is equipped with a second modulator 5 .

[0043] In a specific embodiment, if Figure 4 As shown, the second multimode interferometer 4 adopts a 4×4 multimode interferometer (MMI); wherein the 4×4 multimode interferometer has 4 input ports and 4 output ports. Preferably, in order to reduce insertion loss, the width W_MMI of the second multimode interferometer 4 is 16um, and the length L_MMI of the second multimode interferometer 4 is 466.52um. The taper length L_taper of each port of the second multimode interferometer 4 is 30um, and the taper width W_taper of each port of the second multimode interferometer 4 is 3um. After simulation, the insertion loss is 0.11dB.

[0044] In one embodiment, for the convenience of description, the second multimode interferometer in the forward propagation training layer A is referred to as the second multimode interferometer A, the second multimode interferometer in the forward propagation training layer B is referred to as the second multimode interferometer B, the second multimode interferometer in the forward propagation training layer C is referred to as the second multimode interferometer C, and the second multimode interferometer in the forward propagation training layer D is referred to as the second multimode interferometer D.

[0045] like Figure 1 , Figure 3 as well as Figure 4As shown, the two first output waveguides 2 are connected to any two input ports of the second multimode interferometer A in one-to-one correspondence, and the four output ports of the second multimode interferometer A are respectively connected to a second output waveguide 7. The four second output waveguides 7 connected to the second multimode interferometer A are connected to the four input ports of the second multimode interferometer B in one-to-one correspondence. The four output ports of the second multimode interferometer B are respectively connected to a second output waveguide 7. The four second output waveguides 7 connected to the second multimode interferometer B are connected to the four input ports of the second multimode interferometer C in one-to-one correspondence. The four output ports of the second multimode interferometer C are respectively connected to a second output waveguide 7. The four second output waveguides 7 connected to the second multimode interferometer C are connected to the four input ports of the second multimode interferometer D in one-to-one correspondence.

[0046] In a specific implementation, the length L_heater of the second phase modulator is 250 um, and the width W_heater of the second phase modulator is 3 um. π About 5V.

[0047] In one embodiment, four second multimode interferometers in the multimode interference cascade network and second output waveguides connected to the four second multimode interferometers are all arranged in the silicon device layer, and each second modulator in the multimode interference cascade network is arranged in the modulator layer.

[0048] In one embodiment, the second modulator may be a thermo-optic modulator or an electro-optic modulator, which is not limited in the present invention.

[0049] The following will describe the light propagation path in conjunction with the accompanying drawings: Figure 1 as well as Figure 2 As shown, the coherent light is input from the input grating and then split by the first multimode interferometer 1. The first modulator 3 phase modulates one of the two light beams output from the first multimode interferometer 1 to form simulated signal light, and the other light beam is used as reference light.

[0050] For the convenience of description, the second multimode interferometer in the forward propagation training layer A is referred to as the second multimode interferometer A, the second multimode interferometer in the forward propagation training layer B is referred to as the second multimode interferometer B, the second multimode interferometer in the forward propagation training layer C is referred to as the second multimode interferometer C, and the second multimode interferometer in the forward propagation training layer D is referred to as the second multimode interferometer D.

[0051] Further, the simulated signal light can be input from the first port of the second multimode interferometer A, and the reference light can be input from the third port of the second multimode interferometer A. The second multimode interferometer A interferes with the simulated signal light and the reference light, and the four-way light output by the second multimode interferometer A is phase-modulated by the four second modulators of the forward propagation training layer A and transmitted to the second multimode interferometer B. The second multimode interferometer B interferes with the four-way light entering the second multimode interferometer B, and the four-way light output by the second multimode interferometer B is phase-modulated by the four second modulators of the forward propagation training layer B and transmitted to the second multimode interferometer C. The second multimode interferometer C interferes with the four-way light entering the second multimode interferometer C, and the four-way light output by the second multimode interferometer C is phase-modulated by the four second modulators of the forward propagation training layer C and transmitted to the second multimode interferometer D. The second multimode interferometer D interferes with the four lights entering the second multimode interferometer D. The four lights output by the second multimode interferometer D are phase-modulated by the four second modulators of the forward propagation training layer D and then coupled out through the output grating 6 to form four lights with different phases.

[0052] In one embodiment, the specific process of using the gradient descent algorithm to train the initial optical neural network chip to obtain an optical neural network chip configured with an optimal transmission matrix includes: obtaining the phase loaded by each second modulator in the initial multi-mode interference cascade network to obtain an initial transmission matrix; based on the initial transmission matrix, using the gradient descent algorithm to obtain the optimal transmission matrix; according to the optimal transmission matrix, phase-controlling each second modulator in the initial multi-mode interference cascade network to obtain an optical neural network chip configured with an optimal transmission matrix. It should be noted that an untrained optical neural network chip is defined as an initial optical neural network chip.

[0053] Specifically, before training the optical neural network chip, the wavelength of the coherent light input to the optical neural network chip is determined, and the phase loaded by each second modulator in the initial multi-mode interference cascade network is obtained. The phase loaded by each second modulator constitutes an initial transmission matrix; the optimal transmission matrix is ​​obtained based on the initial transmission matrix through the ADAM gradient descent algorithm.

[0054] The ADAM gradient descent algorithm uses the loss function shown in the following formula 1:

[0055]

[0056] Where a represents the custom parameter for controlling the equivalent common mode rejection ratio, b represents the custom parameter for controlling the phase error, and I imax I represents the normalized maximum value of the i-th output of the optical neural network chip after the input phase sweep; iminRepresents the normalized minimum value of the i-th output of the optical neural network chip after the input phase sweep. i It represents the phase of the i-th output of the optical neural network chip after the input phase is scanned and the fast Fourier transform (FFT) is performed.

[0057] Furthermore, the phase of each second modulator in the initial multi-mode interference cascade network is controlled according to the optimal transmission matrix to obtain an optical neural network chip configured with the optimal transmission matrix. For example, the second modulator is a thermo-optical modulator. By changing the temperature of the thermo-optical modulator, the phase of the light in the corresponding second transmission waveguide is changed.

[0058] Furthermore, after all the second modulators are regulated, the second modulators are no longer regulated during use. Those skilled in the art can obtain outputs of different optical neural network chips by regulating the first modulators to meet usage requirements.

[0059] In a specific embodiment, if Figure 5 As shown, the results of the optical neural network chip after training at a wavelength of 1550nm are shown. After changing the phase of the input signal, the intensity of the four output lights of the optical neural network chip changes with the phase of the input signal and is compared with the theoretical target of an ideal 90° optical mixer.

[0060] In a specific embodiment, if Figure 6 As shown, the relative phase error performance of the optical neural network chip trained at 1550nm wavelength in the 1530nm-1570nm band is demonstrated. i The difference between the phase output at port i and the expected phase at port i is less than ±5° in the range of 1530nm-1560nm, and the phase error is less than ±2° at the target wavelength of 1550nm.

[0061] In a specific embodiment, if Figure 7 As shown, the equivalent common mode rejection ratio performance of the optical neural network chip in the 1530nm-1570nm band after training at a wavelength of 1550nm is shown. At 1550nm, it is less than -40dB, and in the range of 1530nm-1570nm, it is less than -20dB.

[0062] It should be noted that the present application is compatible with a variety of semiconductor process platforms, is independent of modulation methods, is conducive to cost reduction, and has strong scalability. Compared with the existing 90° optical mixer, the present application has better processing robustness. By training the optical neural network chip, it can optimize parameters such as phase error and common mode rejection ratio, and does not rely on complex structural design and low-error processing methods. The present application can compensate for the imbalance caused by the coupling of the four output ports by regulating the on-chip light without relying on subsequent signal processing.

[0063] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0064] In summary, the present application provides an integrated optical 90-degree optical mixer based on an optical neural network, including an optical neural network chip configured with an optimal transmission matrix, which is used to process the received coherent light, generate simulated signal light and reference light, and mix the simulated signal light and reference light to form four paths of light with different phases; wherein, the initial optical neural network chip is trained using a gradient descent algorithm to obtain an optical neural network chip configured with an optimal transmission matrix. The present application uses a gradient descent algorithm to fit the transmission matrix of the optical neural network chip to the transmission matrix of the 90-degree optical mixer to achieve the 90-degree optical mixer task. The optical 90-degree optical mixer of the present application is trainable and no longer relies on complex structural design and low-error processing methods, has better robustness, and can compensate for the imbalance of output coupling of different optical paths. Therefore, the present application effectively overcomes the various shortcomings of the prior art and has a high industrial utilization value.

[0065] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. An integrated optical 90-degree optical mixer based on optical neural network, characterized in that: include: An optical neural network chip configured with an optimal transmission matrix is ​​used to process received coherent light to generate simulated signal light and reference light, and mix the simulated signal light and reference light to form four paths of light with different phases; Among them, a gradient descent algorithm is used to train the initial optical neural network chip to obtain an optical neural network chip configured with an optimal transmission matrix.

2. The integrated optical 90-degree optical mixer based on optical neural network according to claim 1, characterized in that: The optical neural network chip includes a beam splitting structure and a multi-mode interference cascade network; wherein the beam splitting structure is used to generate simulated signal light and reference light; and the multi-mode interference cascade network is used to mix the simulated signal light and reference light.

3. The integrated optical 90-degree optical mixer based on optical neural network according to claim 2, characterized in that: The beam splitting structure includes a first multimode interferometer; the first multimode interferometer is connected to two first output waveguides, and one of the first output waveguides is equipped with a first modulator; wherein one path of light output by the first multimode interferometer is controlled by the first modulator to form a simulated signal light, and the other path of light is used as a reference light.

4. The integrated optical 90-degree optical mixer based on optical neural network according to claim 3, characterized in that: The first multi-mode interferometer has a width of 8 microns and a length of 58.248 microns.

5. The integrated optical 90-degree optical mixer based on optical neural network according to claim 2, characterized in that: The multi-mode interference cascade network is provided with a plurality of forward propagation training layers in sequence along the propagation direction of light.

6. The integrated optical 90-degree optical mixer based on optical neural network according to claim 5, characterized in that: Each forward propagation training layer is composed of a second multi-mode interferometer and four second modulators; wherein the second multi-mode interferometer is connected to four second output waveguides, and each second output waveguide is equipped with a second modulator.

7. The integrated optical 90-degree optical mixer based on optical neural network according to claim 6, characterized in that: The specific process of using the gradient descent algorithm to train the initial optical neural network chip to obtain an optical neural network chip configured with an optimal transmission matrix includes: Obtaining the phase loaded by each second modulator in the initial multi-mode interference cascade network to obtain an initial transmission matrix; Based on the initial transmission matrix, using a gradient descent algorithm, an optimal transmission matrix is ​​obtained; The phase of each second modulator in the initial multi-mode interference cascade network is controlled according to the optimal transmission matrix to obtain an optical neural network chip configured with the optimal transmission matrix.

8. The integrated optical 90-degree optical mixer based on optical neural network according to claim 6, characterized in that: Each second multi-mode interferometer has a width of 16 micrometers and a length of 466.52 micrometers.

9. The integrated optical 90-degree optical mixer based on optical neural network according to claim 6, characterized in that: The tapered length of each port of each second multi-mode interferometer is 30 microns and the width is 3 microns.

10. The integrated optical 90-degree optical mixer based on optical neural network according to claim 6, characterized in that: Each second modulator has a length of 250 microns and a width of 3 microns.

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