Film lithium niobate modulator control system based on intelligent learning and operation method thereof

Abstract

The invention discloses a thin film lithium niobate modulator control system based on intelligent learning and an operation method. The system comprises a laser, a thin film lithium niobate modulator, an optical coupler, an optical fiber, a photoelectric detector, a gain control module, an analog-to-digital converter, a digital-to-analog converter, a signal processing module, a temperature acquisition module, a power acquisition module, a filter and an amplifier. According to the scheme, the temperature and output power of the thin film lithium niobate modulator and the input power of the laser are collected in real time, whether the temperature and output power exceed the knowledge base range is detected, and if not, operational analysis is carried out; if so, entering intelligent learning; a decision result generated by operation is compared with an output result, a new round of flow is entered when a target is reached, a direct current bias control strategy is entered when the target is not reached, feedback signals are formed through control words and digital-to-analog conversion, Vbias is adjusted to be compared with the output result, the new round of flow is entered when the target is reached, and the direct current bias control strategy is entered when the target is not reached; and the modulator reaches the optimal working state.

Description

Technical Field

[0001] The present invention belongs to the technical field of electro-optic modulators, and in particular relates to a thin-film lithium niobate modulator control system based on intelligent learning and an operating method thereof. Background Art

[0002] Thin-film lithium niobate electro-optic modulators are devices that modulate optical signals through the electro-optic effect of lithium niobate. They are widely used in radar, technical reconnaissance, electronic countermeasures and other fields. However, there are great requirements for the broadband range, low disturbance and high stability of thin-film lithium niobate electro-optic modulators in different working environments. The refractive index of thin-film lithium niobate materials changes with the working environment factors such as temperature, mechanical distortion, mechanical vibration, input optical power and polarization state change, which causes the working state of the thin-film lithium niobate modulator to shift, and then causes the output optical signal of the electro-optic modulator to be distorted.

[0003] In the prior art, only a single factor is managed and controlled. For example, the invention patent with publication number CN 117389068 A records a bias voltage control device and method for a lithium niobate thin film thermal modulator. In the device, the lithium niobate thin film thermal modulator generates scattered light; the sensor module is installed on the lithium niobate thin film thermal modulator, the temperature sensor measures the operating temperature of the lithium niobate thin film thermal modulator to generate a temperature voltage signal, and the MPD photodetector detects the scattered light to generate an electrical signal containing a pilot and harmonic components; the signal conditioning module is connected to the sensor module to generate an electrical signal containing a pilot fundamental wave and harmonic components based on a current signal containing a pilot signal and a temperature voltage signal, the MCU data processing module is connected to the signal conditioning module to generate a DC bias signal and a pilot signal at the next moment based on the electrical signal of the pilot fundamental wave and harmonic components, and the bias voltage compensation unit module is connected to the MCU data processing module. The DC bias signal and the pilot signal are input into the lithium niobate thin film thermal modulator after analog addition operation to complete the compensation of the bias voltage. The above scheme does not perform calculation, analysis and comparison on the temperature, input power and output power of the modulator to form feedback adjustment control, thereby improving the control accuracy of the modulator and achieving the optimal working state of stable DC bias; and does not form control based on an intelligent learning environment, which affects the stability of the control of the thin film lithium niobate modulator. Summary of the invention

[0004] The purpose of the present invention is to provide a thin film lithium niobate modulator control system based on intelligent learning and its operation method, which solves the problem of keeping the thin film lithium niobate modulator in real time to achieve the optimal working state of stable DC bias under different environments, and effectively improves the low disturbance and stability of the modulator.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] A thin film lithium niobate modulator control system based on intelligent learning, comprising:

[0007] A laser, used for outputting an optical signal, an output end of which is connected to a thin-film lithium niobate modulator;

[0008] A thin-film lithium niobate modulator modulates the radio frequency electrical signal into an optical signal, and its output end is connected to an optical coupler;

[0009] Optical coupler, which divides the output optical modulation signal of thin-film lithium niobate modulator into two paths, one is the main optical modulation signal, which is used in optical communication system, and the other is the collection optical modulation signal, which enters the intelligent learning thin-film lithium niobate modulator control system and is used to keep the thin-film lithium niobate modulator in real time to achieve the best working state of stable DC bias under different environments;

[0010] Optical fiber, used for long-distance transmission of optical modulated signals, connected to the main photoelectric detector;

[0011] The photoelectric detector includes a main photoelectric detector and a collection photoelectric detector. The main photoelectric detector acts on the optical communication system to demodulate the optical modulation signal into a radio frequency signal, and the collection photoelectric detector converts a small amount of output optical modulation signal into a radio frequency signal of the thin-film lithium niobate modulator control system of intelligent learning;

[0012] The gain control module amplifies the radio frequency signal output by the acquisition photodetector;

[0013] The analog-to-digital converter converts the radio frequency signal amplified by the gain control module into a digital signal and outputs it to the signal processing module;

[0014] The temperature acquisition module is placed on the thin-film lithium niobate modulator chip substrate to collect temperature change information of the thin-film lithium niobate modulator in real time and output it to the signal processing module;

[0015] The power acquisition module collects the laser output power change information and outputs it to the signal processing module;

[0016] The signal processing module processes the collected information of different working environments, including processing temperature changes and input and output power, and outputs feedback signals to enable the optical modulator to achieve the best working state;

[0017] A digital-to-analog converter converts digital signals into analog signals;

[0018] Filter, which filters the electrical signal to reduce signal interference;

[0019] The amplifier amplifies the feedback signal and finally realizes the control of the working state of the thin film lithium niobate modulator.

[0020] Preferably, the best working state is the position where the output characteristic curve of the modulator after photoelectric conversion is zero after the second differentiation.

[0021] Preferably, the splitting ratio of the optical coupler is (95-99) / (5-1).

[0022] The operation method of the thin film lithium niobate modulator control system based on intelligent learning comprises the following steps:

[0023] (1) Module initialization: Initialize each module of the control system;

[0024] (2) Working environment: Collect environmental variables, including temperature and input power; detect whether the temperature and input power exceed the knowledge base range. If they exceed the knowledge base range, enter intelligent learning, i.e., step (3); if they do not exceed the knowledge base range, enter the operation program, i.e., step (4);

[0025] (3) Intelligent learning: Collect external environmental variables to form preliminary knowledge, record modulator parameters under various environmental variables, and form a knowledge base;

[0026] (4) Modulator parameter calculation: Calculate the modulator parameters under the environmental variables;

[0027] (5) Decision result: After calculation, the decision result A is obtained. X ;

[0028] (6) Output result: By collecting a small amount of light modulation signal formed by the photodetector, the gain control module and the analog-to-digital converter, an output result A is obtained;

[0029] (7) Compare: Compare the output result A with the decision result A X Compare and return to step (2) if the target is reached and enter a new round of process; if the target is not reached, enter the DC bias control strategy, i.e. step (8);

[0030] (8) DC bias control strategy: output result A and decision result A X Compare the values. If the values ​​exceed the threshold, the strategy of gradually approaching the target value is adopted, and then the process returns to step (2). If the values ​​are less than the threshold, the strategy of directly achieving the target value is adopted.

[0031] (9) Control word: Convert the target value strategy into a digital signal to obtain a control word;

[0032] (10) Digital-to-analog conversion: Convert the digital signal that implements the target value strategy into an analog signal to form a feedback signal V bias ;

[0033] (11) Feedback control: Adjust V bias , and then compare it with the output result A. If the target is achieved, it enters a new round of process. If the target is not achieved, it jumps to (8) until the optical modulator reaches the optimal working state.

[0034] Preferably, in step (3), the intelligent learning is to first judge the external environmental variables when the input power P0 and temperature T0 of the external environmental variables exceed the range or interval corresponding to the knowledge base. If the environmental variables jump or exceed the linear range, the external environmental variables are collected again; if not, the power coefficient corresponding to the external environmental variable input power P0 is α P0 , the temperature coefficient corresponding to the environmental variable temperature value T0 is β T0 Join the knowledge base.

[0035] Preferably, in step (4), the operation is

[0036] Measure the input power value Px between the knowledge base P1 and P2 values, and find the corresponding power coefficient α P1 ~α P2 Between, through the formula: α Py =α P1 +(α P1 +α P2 ) / P1+P2

[0037] Calculate the power coefficient α corresponding to Py Py , gradually approaching Px, when Py=Px, corresponding to α Py =α Px ,

[0038] The actual measured temperature value Tx is between T1 and T2, and the corresponding temperature coefficient is β T1 ~β T2 Between, through the formula: β Ty =β T1 +(β T1 +β T2 ) / T1+T2

[0039] Calculate the power coefficient β corresponding to Ty Ty , gradually approaching Tx, when Ty=Tx, corresponding to β Ty =β Tx ,

[0040] By the formula Ax = α Px Px+β Tx Tx, calculate the decision result A X , that is, the control word Ax.

[0041] Compared with the prior art, the present invention has the following advantages:

[0042] The present invention collects the temperature of the thin film lithium niobate modulator, the laser input power and the output power (i.e., the output result) of the thin film lithium niobate modulator in real time, detects whether the temperature and the input power exceed the range of the knowledge base, and performs calculation analysis on the temperature and the input power if they do not exceed the range of the knowledge base; enters intelligent learning for analysis and integration if they exceed the range of the knowledge base, collects external environmental variables to form preliminary knowledge, and records the knowledge base, and then enters calculation analysis; compares the decision result generated by the calculation analysis with the output result, enters a new round of process if the target is achieved, and enters a DC bias control strategy if the target is not achieved, and forms a feedback signal through a control word and digital-to-analog conversion to adjust V bias Compare with the output result, enter a new round of process if the target is achieved, and enter the DC bias control strategy again if the target is not achieved until the modulator reaches the optimal working state. The present invention is based on the micro-perturbation thin film lithium niobate modulator control system under the intelligent learning working environment, which realizes the optimal working state of the optical modulator in real time and accurately controls the temperature and power changes of the thin film lithium niobate modulator in real time, and effectively improves the low disturbance and stability of the modulator. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a module block diagram of the system of the present invention;

[0044] Figure 2 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0046] like Figure 1 As shown, the present invention discloses a thin film lithium niobate modulator control system based on intelligent learning, including a laser, a thin film lithium niobate modulator, an optical coupler, an optical fiber, a photodetector, a gain control module (MAC), an analog-to-digital converter (A / D), a digital-to-analog converter (D / A), a signal processing module (CPU), a temperature acquisition module, a power acquisition module, a filter and an amplifier.

[0047] The laser is used to output a 12dBm optical signal, which enters a thin-film lithium niobate modulator, and an input radio frequency signal (frequency range is 1MHz-40GHz) is modulated onto the optical signal to become an optical modulation signal. The thin-film lithium niobate modulator modulates the radio frequency electrical signal onto the optical signal, and its output end is connected to an optical coupler. The optical coupler divides the optical modulation signal output by the thin-film lithium niobate modulator into two paths, one of which is the main optical modulation signal, which is applied to the optical communication system, and the other is the collection optical modulation signal, which enters the intelligent learning thin-film lithium niobate modulator control system and is applied to keep the thin-film lithium niobate modulator in real time in a stable DC bias optimal working state under different environments. The optical fiber is used to transmit the optical modulation signal over long distances and is connected to the main photoelectric detector. The photoelectric detector includes a main photoelectric detector and a collection photoelectric detector. The main photoelectric detector acts on the optical communication system to demodulate the optical modulation signal into a radio frequency signal, and the collection photoelectric detector converts a small amount of output optical modulation signal into a radio frequency signal of the intelligent learning thin-film lithium niobate modulator control system. The gain control module amplifies the radio frequency signal output by the collection photoelectric detector. The analog-to-digital converter converts the radio frequency signal amplified by the gain control module into a digital signal and outputs it to the signal processing module. The temperature acquisition module is placed on the thin-film lithium niobate modulator chip substrate to collect temperature change information of the thin-film lithium niobate modulator in real time and output it to the signal processing module. The power acquisition module collects laser output power change information and outputs it to the signal processing module. The signal processing module processes the information collected in different working environments, including processing temperature changes and input and output power, and outputs feedback signals to enable the optical modulator to reach the optimal working state. The digital-to-analog converter converts digital signals into analog signals. The filter filters the electrical signal to reduce signal interference. The amplifier amplifies the feedback signal and ultimately realizes the control of the working state of the thin-film lithium niobate modulator.

[0048] The present invention is based on a micro-perturbation thin film lithium niobate modulator control system under an intelligent learning working environment, and its data includes real-time acquisition of thin film lithium niobate modulator temperature, laser input power and thin film lithium niobate modulator output power. Among them, the temperature acquisition module is used to realize the real-time acquisition of the temperature of the thin film lithium niobate modulator. The power acquisition module is used to realize the real-time acquisition of the laser input power. The acquisition photoelectric detector, gain control module and analog-to-digital converter are used to realize the real-time acquisition of the output power of the thin film lithium niobate modulator. The signal processing module is used to analyze the collected working environment factors and output a feedback signal to make the optical modulator reach the best working state. The feedback signal controls the working state of the thin film lithium niobate modulator through a digital-to-analog converter, a filter and an amplifier. The optimal working state is the position where the electro-optic modulator outputs a characteristic curve of 0 after the second differentiation after photoelectric conversion under the condition that the optical signal works (the point with the best linearity); the splitting ratio of the optical coupler is (95~99) / (5~1).

[0049] The thin-film lithium niobate modulator control system based on intelligent learning of the present invention mainly performs the following functions:

[0050] The laser outputs a 12dBm optical signal; the optical signal enters the thin-film lithium niobate modulator, and the input RF signal (frequency range is 1MHz-40GHz) is modulated onto the optical signal to become an optical modulated signal; the optical modulated signal is divided into 95 / 5 by an optical coupler; 95% of the optical modulated signal is transmitted over long distances through optical fiber (up to 20km); the optical modulated signal is converted into an RF signal by a main photoelectric detector. The optical coupler is divided into 5% optical modulation signals; the 5% optical modulation signals are converted into radio frequency signals through the acquisition photoelectric detector; the radio frequency signals are amplified through the gain control module; the radio frequency signals are converted into digital signals through the analog-to-digital converter; the digital signals are put into the signal processing module to obtain the output optical power parameters of the thin film lithium niobate modulator; the temperature of the thin film lithium niobate modulator is collected through the temperature acquisition module and imported into the signal processing module to obtain the temperature parameters of the thin film lithium niobate modulator; the laser output power parameters are collected through the power acquisition module and imported into the signal processing module to obtain the laser output power parameters; the signal processing module processes the working environment parameters (the output optical power parameters of the thin film lithium niobate modulator, the temperature parameters of the thin film lithium niobate modulator, and the laser output power parameters) and outputs a feedback signal; the digital signals are converted into radio frequency signals through the digital-to-analog converter; the radio frequency signal interference is filtered out through the filter; the radio frequency signal is amplified through the amplifier; and the radio frequency signal is input into the thin film lithium niobate modulator V bias Adjust the working state of the thin film lithium niobate modulator.

[0051] like Figure 2 As shown, the operation method of the thin film lithium niobate modulator control system based on intelligent learning of the present invention comprises the following steps:

[0052] (1) Initializing each control system module, including initializing the information processing module, initializing the analog-to-digital converter of the signal extraction module, the gain control module, the temperature acquisition module and the power acquisition module, and initializing the control variables of the microprocessor.

[0053] (2) Collect environmental variables, including temperature, input power, and output power; detect whether the temperature and input power exceed the scope of the knowledge base. If they exceed the scope of the knowledge base, enter the intelligent learning; if they do not exceed the scope of the knowledge base, enter the operation program;

[0054] (3) Intelligent learning: Collect external environmental variables. When the external environmental variable input power P0 and temperature T0 exceed the range or interval corresponding to the knowledge base, first judge the external environmental variables. If the environmental variable jumps or exceeds the linear range, perform secondary collection of external environmental variables. If not, set the power coefficient corresponding to the external environmental variable input power P0 to αP0 and the temperature coefficient corresponding to the environmental variable temperature value T0 to β T0 Join the knowledge base.

[0055] (4) Modulator parameter calculation: Calculate the modulator parameters under the environment variables. Specifically, the input power value Px is measured between the values ​​P1 and P2 in the knowledge base, and the corresponding power coefficient is found to be α P1 ~α P2 Between, through the formula: α Py =α P1 +(α P1 +α P2 ) / P1+P2

[0056] Calculate the power coefficient α corresponding to Py Py , gradually approaching Px, when Py=Px, corresponding to α Py =α Px ,

[0057] The actual measured temperature value Tx is between T1 and T2, and the corresponding temperature coefficient is β T1 ~β T2 Between, through the formula: β Ty =β T1 +(β T1 +β T2 ) / T1+T2

[0058] Calculate the power coefficient β corresponding to Ty Ty , gradually approaching Tx, when Ty=Tx, corresponding to β Ty =β Tx , through the formula Ax = α Px Px+β Tx Tx, calculate the decision result A X , that is, the control word Ax.

[0059] (5) Decision result: After calculation, the decision result A is obtained. X ;

[0060] (6) Output result: By collecting a small amount of light modulation signal formed by the photodetector, the gain control module and the analog-to-digital converter, an output result A is obtained;

[0061] (7) Compare: Compare the output result A with the decision result A XCompare and return to step (2) if the target is reached and enter a new round of process; if the target is not reached, enter the DC bias control strategy, i.e. step (8);

[0062] (8) DC bias control strategy: output result A and decision result A X Compare the values. If the values ​​exceed the threshold, the strategy of gradually approaching the target value is adopted, and then the process returns to step (2) to make large-scale adjustments. If the values ​​are less than the threshold, the strategy of directly achieving the target value is adopted, that is, small-scale adjustments are made.

[0063] (9) Control word: Convert the target value strategy into a digital signal to obtain a control word;

[0064] (10) Digital-to-analog conversion: Convert the digital signal that implements the target value strategy into an analog signal to form a feedback signal V bias ;

[0065] (11) Feedback control: Adjust V bias , and then compare it with the output result A. If the target is achieved, it enters a new round of process. If the target is not achieved, it jumps to (8) until the optical modulator reaches the optimal working state.

[0066] The above embodiments are only specific examples for further describing the purpose, technical solutions and beneficial effects of the present invention, and the present invention is not limited thereto. Any modification, equivalent replacement, improvement, etc. made within the scope of the disclosure of the present invention are included in the protection scope of the present invention.

Claims

1. Thin-film lithium niobate modulator control system based on intelligent learning, characterized by: include A laser, used for outputting an optical signal, an output end of which is connected to a thin-film lithium niobate modulator; A thin-film lithium niobate modulator modulates the radio frequency electrical signal into an optical signal, and its output end is connected to an optical coupler; Optical coupler, which divides the output optical modulation signal of thin-film lithium niobate modulator into two paths, one is the main optical modulation signal, which is used in optical communication system, and the other is the collection optical modulation signal, which enters the intelligent learning thin-film lithium niobate modulator control system and is used to keep the thin-film lithium niobate modulator in real time to achieve the best working state of stable DC bias under different environments; Optical fiber, used for long-distance transmission of optical modulated signals, connected to the main photoelectric detector; The photoelectric detector includes a main photoelectric detector and a collection photoelectric detector. The main photoelectric detector acts on the optical communication system to demodulate the optical modulation signal into a radio frequency signal, and the collection photoelectric detector converts a small amount of output optical modulation signal into a radio frequency signal of the thin-film lithium niobate modulator control system of intelligent learning; The gain control module amplifies the radio frequency signal output by the acquisition photodetector; The analog-to-digital converter converts the radio frequency signal amplified by the gain control module into a digital signal and outputs it to the signal processing module; The temperature acquisition module is placed on the thin-film lithium niobate modulator chip substrate to collect temperature change information of the thin-film lithium niobate modulator in real time and output it to the signal processing module; The power acquisition module collects the laser output power change information and outputs it to the signal processing module; The signal processing module processes the collected information of different working environments, including processing temperature changes and input and output power, and outputs feedback signals to enable the optical modulator to achieve the best working state; Digital-to-analog converter, which converts digital signals into analog signals; Filter, which filters the electrical signal to reduce signal interference; The amplifier amplifies the feedback signal and finally realizes the control of the working state of the thin film lithium niobate modulator.

2. The thin film lithium niobate modulator control system based on intelligent learning according to claim 1 is characterized in that: The best working state is the position where the output characteristic curve of the modulator after photoelectric conversion is zero after the second differentiation.

3. The thin film lithium niobate modulator control system based on intelligent learning according to claim 1, characterized in that: The splitting ratio of the optical coupler is (95-99) / (5-1).

4. The operation method of the thin film lithium niobate modulator control system based on intelligent learning is characterized in that: The following steps are involved: (1) Module initialization: Initialize each module of the control system; (2) Working environment: Collect environmental variables, including temperature and input power; detect whether the temperature and input power exceed the knowledge base range. If they exceed the knowledge base range, enter intelligent learning, i.e., step (3); if they do not exceed the knowledge base range, enter the operation program, i.e., step (4); (3) Intelligent learning: Collect external environmental variables to form preliminary knowledge, record modulator parameters under various environmental variables, and form a knowledge base; (4) Modulator parameter calculation: Calculate the modulator parameters under the environmental variables; (5) Decision result: After calculation, the decision result A is obtained. X ; (6) Output result: By collecting a small amount of light modulation signal formed by the photodetector, the gain control module and the analog-to-digital converter, an output result A is obtained; (7) Compare: Compare the output result A with the decision result A X Make a comparison, and when the goal is achieved, return to step (2) and enter a new round of process; If the target is not reached, the DC bias control strategy is entered, i.e., step (8); (8) DC bias control strategy: output result A and decision result A X Compare the values. If the values ​​exceed the threshold, the strategy of gradually approaching the target value is adopted, and then the process returns to step (2). If the values ​​are less than the threshold, the strategy of directly achieving the target value is adopted. (9) Control word: Convert the target value strategy into a digital signal to obtain a control word; (10) Digital-to-analog conversion: Convert the digital signal that implements the target value strategy into an analog signal to form a feedback signal V bias ; (11) Feedback control: Adjust V bias , and then compare it with the output result A. If the target is achieved, it enters a new round of process. If the target is not achieved, it jumps to (8) until the optical modulator reaches the optimal working state.

5. The operating method of the thin film lithium niobate modulator control system based on intelligent learning according to claim 4, characterized in that: In step (3), the intelligent learning is to first judge the external environmental variables when the input power P0 and temperature T0 of the external environmental variables exceed the range or interval corresponding to the knowledge base. If the environmental variables jump or exceed the linear range, the external environmental variables are collected again; if not, the power coefficient corresponding to the external environmental variable input power P0 is α P0 , the temperature coefficient corresponding to the environmental variable temperature value T0 is β T0 Join the knowledge base.

6. The operating method of the thin film lithium niobate modulator control system based on intelligent learning according to claim 4, characterized in that: In step (4), the operation is Measure the input power value Px between the knowledge base P1 and P2 values, and find the corresponding power coefficient α P1 ~α P2 Between, through the formula: α Py =α P1 +(α P1 +α P2 ) / P1+P2 Calculate the power coefficient α corresponding to Py Py , gradually approaching Px, when Py=Px, corresponding to α Py =α Px , The actual measured temperature value Tx is between T1 and T2, and the corresponding temperature coefficient is β T1 ~β T2 Between, through the formula: β Ty =β T1 +(β T1 +β T2 ) / T1+T2 Calculate the power coefficient β corresponding to Ty Ty , gradually approaching Tx, when Ty=Tx, corresponding to β Ty =β Tx , By the formula Ax = α Px Px+β Tx Tx, calculate the decision result A X , that is, the control word Ax.

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