Automatic half-wave voltage detection device and method for crystal modulation experiment

Abstract

The invention discloses an automatic half-wave voltage detection device and method for a crystal modulation experiment, and particularly relates to the technical field of photoelectric measurement. Comprising a tunable laser source (the wavelength of 560-750 nm is adjustable), a crystal modulation module (a high-voltage direct-current power supply and a signal source), an integrated detection module (a horizontal rotating support integrated power meter and a photoelectric converter) and a control module (double data acquisition cards and a Python program). The function implementation mode is as follows: the synchronous measurement of an extreme value method (extracting extreme points by moving average fitting of a U-P curve) and a frequency multiplication method (capturing frequency multiplication distortion points by spectral analysis) is automatically executed through a rotary bracket switching detection unit (the alignment precision is less than or equal to 0.1 mm); and a lambda-Upi relation curve is generated in combination with multi-wavelength scanning, and is compared with a theoretical formula Upi = lambda d / (2n03gamma22L) for analysis. According to the invention, manual intervention and optical path dismounting errors are eliminated, full-process closed-loop control is realized, and high-precision data support is provided for parameter optimization of the electro-optical modulator.

Description

Technical Field

[0001] The present invention relates to the field of photoelectric measurement technology, and more particularly to a method for automatically detecting a half-wave voltage in a crystal modulation experiment. Background Art

[0002] The performance stability of electro-optical modulators directly depends on the accuracy of the lithium niobate crystal's half-wave voltage (Uπ), which reflects the crystal's ability to change the optical phase under an external electric field. Traditional experiments use manual adjustment of the DC voltage (-1200V to 1200V range) and manual recording of power meter data. A single measurement takes more than 30 minutes and relies on the operator's experience to determine the location of the extreme point. Existing technologies have three major limitations: First, fixed-wavelength laser sources (such as 632.8nm helium-neon lasers) cannot study the quantitative relationship between wavelength and half-wave voltage, and the theoretical model Uπ=λd / (2n0 3 γ 22 L) clearly demonstrates the linear effect of λ on Uπ; second, voltage regulation, data acquisition, and frequency multiplication verification all require manual intervention, introducing reading errors (typical error ±5V); third, the power meter is separated from the photoelectric conversion module, and repeated disassembly causes optical path deviation (>1mm), requiring recalibration of the optical path and low efficiency. The paper "Study on the Stability of Electro-Optical Modulation Devices" (author et al., 2020) points out that half-wave voltage drift exceeding 2% will cause modulation signal distortion, but there is a lack of automated measurement methods to verify this conclusion;

[0003] The current half-wave voltage measurement method has three defects:

[0004] Functionality Deficiencies: Fixed-wavelength lasers (such as 650nm semiconductor lasers) cannot generate multi-wavelength Uπ curves, making it difficult to optimize the operating wavelength range of electro-optical devices. For example, manually switching between five wavelength laser sources takes over five hours, and optical path consistency cannot be guaranteed.

[0005] Insufficient automation: DC voltage steps rely on knob adjustment (step accuracy is only 1V), power meter data needs to be manually recorded and imported into computer graphics, and the extreme point positioning error is as high as ±3%; the frequency doubling phenomenon needs to be observed with the naked eye using an oscilloscope, and the frequency misjudgment rate exceeds 10%.

[0006] System Discreteness: The power meter and photoelectric converter are installed separately. Switching requires disassembly and reassembly, resulting in an optical axis offset of more than 0.5mm and additional light intensity loss (approximately 15%), which cannot meet the needs of high-precision device development.

[0007] Therefore, in order to solve the above problems, a half-wave voltage automatic detection device method for crystal modulation experiment is proposed. Summary of the Invention

[0008] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a device and method for automatically detecting half-wave voltage in a crystal modulation experiment to solve the problems raised in the above-mentioned background technology.

[0009] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a method for automatically detecting half-wave voltage in a crystal modulation experiment, comprising: constructing a tunable laser source with a wavelength continuously adjustable from 560nm to 750nm, and irradiating the output beam to a lithium niobate crystal; configuring a crystal modulation module, applying a DC bias voltage in the range of -1200V to 1200V with a step accuracy of 0.1V through a high-voltage DC power supply, and at the same time superimposing a sinusoidal AC modulation signal with a frequency of 1Hz-10kHz and an amplitude adjustable from 0-50V by a signal source; arranging an integrated detection module on a horizontally adjustable bracket An optical power meter and a photoelectric converter are integrated. The optical power meter converts the optical signal into a 0-10V analog voltage with an output accuracy of 0.1V. The photoelectric converter converts the modulated optical signal into a 4-20mA analog current. By rotating the bracket, one of the two is precisely aligned with the center of the crystal output light, and the alignment error does not exceed 0.1mm. The automated control process is executed, with the detection signal collected by the first data acquisition card and the control instruction output by the second data acquisition card. Parameter setting, data acquisition and dual-algorithm synchronous measurement are realized based on the Python program, where the dual algorithm includes the extreme value method and the frequency doubling method.

[0010] Preferably, the rotation mechanism of the horizontally adjustable bracket adopts precision thread transmission, and the single rotation angle is controlled within the range of ±5°, so that the center of the power meter photosensitive surface or the photoelectric converter receiving surface coincides with the crystal output optical axis, the light spot center offset is ≤0.1mm, and no optical recalibration is required after switching; when the bracket rotates, the position sensor is automatically triggered and the currently activated detection unit type signal is sent to the control module.

[0011] Preferably, the specific process of the extreme value method measurement is: setting the DC voltage scanning range and step value, applying DC voltage point by point according to the step value and synchronously collecting optical power data; using a sliding average algorithm to process the voltage-power curve, the algorithm window width W and the voltage step value ΔV satisfy W = 5ΔV to maintain a resolution of 0.1V; automatically fitting the quadratic curve function, locating adjacent maximum points U_max and minimum points U_min, and calculating the half-wave voltage experimental value Uπ = |U_max-U_min|.

[0012] Preferably, the specific process of the frequency doubling method verification is: continuously apply an AC modulation signal with a fixed frequency f0, and collect the spectrum of the output current signal of the photoelectric converter in real time; when it is detected that the amplitude of the frequency f0 component drops below 5% of the baseline and the amplitude of the frequency 2f0 component accounts for more than 95%, it is determined that frequency doubling distortion occurs, and the current DC voltage value U_dist is recorded; after continuously identifying two adjacent distortion points, the half-wave voltage verification value Uπ'=|U_dist1-U_dist2| is calculated.

[0013] Preferably, a dual-algorithm collaborative mechanism is established. During the DC voltage scanning process, when the frequency doubling method detects the first distortion point U_dist, the extreme value method data resampling is immediately performed with the U_dist as the center, with the step value range expanded 10 times to both sides; if |Uπ-Uπ'| / Uπ>1%, the photoelectric converter gain is automatically adjusted and the voltage range is rescanned.

[0014] Preferably, a multi-wavelength half-wave voltage relationship analysis is performed, and five or more groups of different wavelengths λi (i = 1, 2, ..., n) are sequentially set, and dual algorithm measurements are performed under each group of wavelengths; the theoretical half-wave voltage value is calculated based on the physical parameters of the lithium niobate crystal, where λi is the laser vacuum wavelength (unit: meter), d = 3×10-3 m is the thickness of the crystal along the electric field direction, L = 35×10-3 m is the crystal light transmission length, n0=2.286 is the zero-field refractive index, γ 22 =6.8×10 -12 m / V is the electro-optic coefficient, and the theoretical formula is: Uπ theoretical value = (λi×d) / (2×n0 3 ×γ 22 ×L), generate the experimental wavelength-half-wave voltage curve, mark the relative error δ = |Uπ experimental value - Uπ theoretical value| / Uπ theoretical value × 100% at each point, and output the error distribution report.

[0015] Preferably, the Python program includes a dynamic monitoring module that displays three core data in real time: a DC voltage scanning progress bar, a curve of optical power changing with voltage, and a photoelectric signal spectrum waterfall diagram; when it is detected that the power meter output voltage is >9.5V or the signal-to-noise ratio of the frequency-doubled signal is <20dB, the measurement is automatically paused and an audible and visual alarm is triggered; a built-in temperature drift compensation algorithm is used to correct the baseline drift caused by the ambient temperature based on historical data.

[0016] A computer-readable storage medium stores executable program code, which implements the above complete process when the code is run, including: laser wavelength parameter configuration interface, detection unit switching control instructions, dual algorithm synchronous measurement thread, crystal physical parameter database (storing d, L, n0, γ 22 constants), multi-wavelength data comparison matrix and error analysis report generation module.

[0017] Technical effects and advantages of the present invention:

[0018] The present invention automatically switches wavelengths through a tunable laser module (560-750nm continuously adjustable), and combines it with a physical parameter database of lithium niobate crystals to achieve automatic measurement of multi-wavelength half-wave voltages and generation of λ-Uπ relationship curves; adopts a horizontal rotating bracket design with an integrated detection module, and switches the power meter and photoelectric converter through a mechanical knob, eliminating the optical path offset problem caused by traditional disassembly, ensuring that the center alignment accuracy of the dual detection units is ≤0.1mm; based on the Python program, the dual data acquisition cards are controlled to synchronously execute the extreme value method and the frequency doubling method: the extreme value method fits the voltage-power curve through a real-time sliding average algorithm and automatically marks the extreme value points, and the frequency doubling method automatically captures the output signal frequency doubling distortion points through spectrum analysis, and the dual algorithm cross-validation improves the reliability of half-wave voltage measurement; combined with MySQL database storage and Grafana visualization platform, it realizes closed-loop control of the entire process from parameter setting, data acquisition to analysis reporting, completely eliminating the manual intervention link, and at the same time, the built-in temperature drift compensation algorithm suppresses environmental interference, providing high-precision data support for wavelength selection and parameter optimization of electro-optical modulation devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a workflow diagram of the present invention;

[0020] Figure 2 It is the overall framework diagram of the present invention;

[0021] Figure 3 It is the system principle diagram of the present invention;

[0022] Figure 4 This is a control module framework diagram of the present invention;

[0023] Figure 5 This is a software architecture diagram of the present invention. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0025] Example 1

[0026] As attached Figure 1-5As shown, (1) a method for automatic detection of half-wave voltage of crystal modulation experiment, comprising: constructing a tunable laser source with a wavelength continuously adjustable from 560nm to 750nm, and irradiating the output beam to a lithium niobate crystal; configuring a crystal modulation module, applying a DC bias voltage in the range of -1200V to 1200V and a step accuracy of 0.1V through a high-voltage DC power supply, and at the same time superimposing a sinusoidal AC modulation signal with a frequency of 1Hz-10kHz and an amplitude adjustable from 0 to 50V by a signal source; setting an integrated detection module, integrating an optical power meter and a photoelectric converter on a horizontally adjustable bracket, the optical power meter converting the optical signal into a 0-10V analog voltage with an output accuracy of 0.1V, and the photoelectric converter converting the modulated optical signal into a 4-20mA analog current, and rotating the bracket so that one of the two is accurately aligned with the center of the crystal output light, with an alignment error of no more than 0.1mm; executing an automated control process, collecting detection signals through a first data acquisition card, and outputting control instructions through a second data acquisition card, and realizing parameter setting, data acquisition and dual-algorithm synchronous measurement based on a Python program, wherein the dual algorithm includes an extreme value method and frequency doubling method, wherein a tunable laser source module is constructed, and a wavelength-tunable semiconductor laser is used to output a continuously tunable laser from 560nm to 750nm, with a wavelength control accuracy of ±0.1nm, and the wavelength data is transmitted to the control center in real time through the RS232 interface; a crystal modulation module is configured, in which a high-voltage DC power supply outputs a DC bias voltage from -1200V to 1200V, with a minimum voltage step value of 0.1V, and a signal source generates a 1Hz-10kHz sinusoidal AC modulation signal with an adjustable amplitude of 0-50V; an integrated detection module is installed, and an optical power meter (outputting 0-10V analog voltage) and a photoelectric converter (outputting 4-20mA current) are integrated into a precision rotating bracket, and the bracket adjusts the horizontal displacement through a threaded guide rod so that the center of the detection unit is aligned with the crystal output optical axis, and the alignment error is ≤0.1mm; an automated control process is executed, the first data acquisition card collects the detection signal at a sampling rate of 10kHz, the second data acquisition card outputs control instructions to the modulation module, the Python program calls the NumPy library to process the data in real time, and the extreme value method and frequency doubling method are executed simultaneously to calculate the half-wave voltage.

[0027] (2) The rotating mechanism of the horizontally adjustable bracket adopts precision thread transmission, and the single rotation angle is controlled within the range of ±5°, so that the center of the power meter photosensitive surface or the photoelectric converter receiving surface coincides with the crystal output optical axis, and the center offset of the light spot is ≤0.1mm. No optical recalibration is required after switching; when the bracket rotates, the position sensor is automatically triggered to send the currently activated detection unit type signal to the control module, wherein the rotating mechanism of the horizontally adjustable bracket adopts M6 precision ball screw with a screw lead of 1mm / revolution, and each 5° rotation angle corresponds to a bracket displacement of 0.07mm; during rotation, the angle change is detected by the Hall sensor, and when it rotates to the preset position (0° for the power meter, 90° for the photoelectric converter), the position signal is triggered and transmitted to the Python program through the GPIO interface; a V-shaped positioning groove is set on the bracket base to engage with the crystal clamp to ensure that the center offset of the light spot is ≤0.1mm after switching, and there is no need to readjust the optical path alignment; the receiving surface of the photoelectric converter is coated with an anti-reflection film to reduce the reflection loss to less than 1%.

[0028] (3) The specific process of the extreme value method measurement is as follows: set the DC voltage scanning range and step value, apply DC voltage point by point according to the step value and collect optical power data synchronously; use the sliding average algorithm to process the voltage-power curve, and the algorithm window width W and the voltage step value ΔV satisfy W = 5ΔV to maintain 0.1V resolution; automatically fit the quadratic curve function, locate the adjacent maximum point U_max and minimum point U_min, and calculate the half-wave voltage experimental value Uπ = |U_max-U_min|. The extreme value method measurement process: set the DC voltage range (such as -600V) in the Python interface To 600V), step value (such as 5V) and sampling time (such as 2 seconds / point); control the high-voltage power supply to output DC voltage point by point according to the step value, and synchronously collect the voltage value of the optical power meter; use the sliding average algorithm to process the data with a window width W = 5 × step value (for example, W = 25 points when stepping 5V) to eliminate environmental noise; call the Savitzky-Golay filter of the SciPy library to fit the voltage-power curve, automatically identify adjacent extreme points (U_max, U_min), and calculate the half-wave voltage Uπ = |U_max-U_min|, and the result is displayed in real time on the instrument panel.

[0029] (4) The specific process of the frequency doubling method verification is as follows: continuously apply an AC modulation signal with a fixed frequency f0, and collect the spectrum of the current signal output by the photoelectric converter in real time; when it is detected that the amplitude of the frequency f0 component drops below 5% of the baseline and the amplitude of the frequency 2f0 component accounts for more than 95%, it is determined that frequency doubling distortion occurs and the current DC voltage value U_dist is recorded; after continuously identifying two adjacent distortion points, the half-wave voltage verification value Uπ'=|U_dist1-U_dist2| is calculated. The frequency doubling method verification process: the signal source outputs an AC modulation signal with a fixed frequency f0 (such as 1kHz); the current signal of the photoelectric converter is input into the FFT analysis module (Python) after I / V conversion. The system calculates spectral components in real time. When the amplitude of the f0 component drops below 5% of the baseline and the amplitude of the 2f0 component accounts for >95% (determined by the formula: A_2f0 / (A_f0+A_2f0)>0.95), it is marked as a double-frequency distortion point and the current DC voltage U_dist is recorded. After continuously identifying two adjacent distortion points, Uπ'=|U_dist1-U_dist2| is calculated and the data is stored in the "Double-Frequency Verification" table in the MySQL database.

[0030] (5) A dual-algorithm collaborative mechanism is established. During the DC voltage scanning process, when the frequency doubling method detects the first distortion point U_dist, the step value range is immediately expanded 10 times on both sides with U_dist as the center to perform extreme value method data resampling; if |Uπ-Uπ'| / Uπ>1%, the photoelectric converter gain is automatically adjusted and the voltage range is rescanned. Among them, the dual-algorithm collaborative mechanism is as follows: when the frequency doubling method detects the first distortion point U_dist, the Python program immediately expands the step value range 10 times on both sides with U_dist as the center (for example, expanding to ±50V when stepping 5V); within this range, the voltage step value is reduced to 1 / 10 of the original value (such as 0.5V), and the optical power data is re-collected to perform extreme value method fitting; if the half-wave voltage deviation |Uπ-Uπ'| / Uπ obtained by the two methods is>1%, the photoelectric converter gain is automatically adjusted (controlled by a digital potentiometer), and the gain adjustment step is 1dB until the deviation is ≤1%.

[0031] (6) Perform multi-wavelength half-wave voltage relationship analysis, set more than 5 groups of different wavelengths λi (i = 1, 2, ..., n) in sequence, and complete dual algorithm measurement under each group of wavelengths; calculate the theoretical half-wave voltage value based on the physical parameters of the lithium niobate crystal, where λi is the laser vacuum wavelength (unit: meter), d = 3×10-3 m is the thickness of the crystal along the electric field direction, L = 35×10-3 m is the crystal light transmission length, n0=2.286 is the zero-field refractive index, γ 22 =6.8×10 -12m / V is the electro-optic coefficient, and the theoretical formula is: Uπ theoretical value = (λi×d) / (2×n0 3 ×γ 22 ×L), generate the experimental wavelength-half-wave voltage curve, mark the relative error δ = |Uπ experimental value - Uπ theoretical value | / Uπ theoretical value × 100% at each point, and output the error distribution report. Among them, the multi-wavelength analysis process is as follows: set the laser wavelength λi in sequence (at least 5 groups, such as 560nm, 600nm, 650nm, 700nm, 750nm), perform dual algorithm measurement at each wavelength group; call the crystal parameter database (d = 0.003m, L = 0.035m, n0 = 2.286, γ 22 =6.8e-12), according to the formula Uπ theoretical value = (λi×d) / (2×n0 3 ×γ 22 ×L) to calculate the theoretical value; the Matplotlib library generates the experimental wavelength-half-wave voltage curve, annotating the relative error δ at each point (δ = |Uπ experimental value - Uπ theoretical value| / Uπ theoretical value × 100%); the output PDF report includes an error distribution histogram and the optimal wavelength recommendation (wavelength range with δ < 2%).

[0032] (7) The Python program includes a dynamic monitoring module that displays three core data in real time: a DC voltage scanning progress bar, a curve of optical power changing with voltage, and a waterfall diagram of the optical signal spectrum; when the power meter output voltage is detected to be >9.5V or the signal-to-noise ratio of the frequency-doubled signal is <20dB, the measurement is automatically paused and an audible and visual alarm is triggered; a built-in temperature drift compensation algorithm is used to correct the baseline drift caused by the ambient temperature based on historical data. The dynamic monitoring module is implemented as follows: a human-machine interface is developed using PyQt5, with a voltage scanning progress bar (0-100%) displayed on the left and a real-time display in the middle. The UP curve (the extreme point is marked in red) is displayed, and the signal spectrum waterfall diagram is displayed on the right. When the power meter output voltage is greater than 9.5V (close to the upper limit of the range) or the multiplier signal noise ratio (SNR) is less than 20dB (calculated by the formula: SNR = 10log10(A_signal / A_noise)), the buzzer alarm is triggered and the measurement is suspended. The temperature drift compensation algorithm uses the AD590 sensor to collect the ambient temperature and correct the baseline according to ΔU_comp = k × (T-25) (k = 0.1V / ℃ is the temperature coefficient, and it is stored in the EEPROM after calibration).

[0033] (8) A computer-readable storage medium storing executable program code, wherein the code is executed to implement the above-mentioned complete process, including: laser wavelength parameter configuration interface, detection unit switching control instructions, dual algorithm synchronous measurement thread, crystal physical parameter database (storing d, L, n0, γ 22constant), multi-wavelength data comparison matrix and error analysis report generation module, wherein the computer readable storage medium stores Python code, including five major functional modules:

[0034] Parameter configuration interface: Tkinter graphical input of wavelength, voltage step and other parameters;

[0035] Detection unit switching control: Send SCPI commands to the rotation bracket controller through the PyVISA library;

[0036] Dual algorithm threads: extreme value method and frequency doubling method are executed in parallel, and queues are used to implement data interaction;

[0037] Crystal parameter database: SQLite table stores d, L, n0, γ 22 Constant and temperature coefficient k;

[0038] Report generation module: The Jinja2 template engine automatically generates an HTML-formatted error analysis report, including data tables and fitting curve graphs.

[0039] Example 2: Measuring the Half-Wave Voltage of a 650nm Lithium Niobate Crystal (Multi-Source Data Joint Simulation Modeling Scenario)

[0040] Step 1: System initialization and parameter configuration phase:

[0041] After starting the Python control program, the fixed parameters of the lithium niobate crystal are automatically loaded from the MySQL database: thickness d = 3.0 mm, optical length L = 35.0 mm, zero-field refractive index n0 = 2.286, electro-optic coefficient γ 22 =6.8×10 -12 The experimental parameters were set using the PyQt5 graphical interface: laser wavelength λi = 650 nm (vacuum wavelength, accuracy ±0.1 nm), DC voltage sweep range -600 V to +600 V (step value ΔV = 5 V), AC modulation signal frequency f0 = 1 kHz, amplitude V_AC = 5 V, sampling parameters set to 2 seconds per voltage point, and a sliding average filter window W = 25 points (dynamically determined according to the formula W = 5 × ΔV).

[0042] Step 2: Detection unit alignment and optical path calibration stage:

[0043] The optical power meter is precisely switched to the 0° working position by rotating the precision screw of the horizontal bracket (lead 1mm / revolution). The Hall sensor detects the bracket angle in real time. When the preset position is reached, it sends a positioning signal to the Python program, triggering a fine-tuning command to completely align the center of the laser spot with the photosensitive surface of the power meter (alignment error ≤ 0.1mm). The laser output power is pre-adjusted to 10mW to ensure that the optical power meter operates in the linear range and avoid detection saturation.

[0044] Step 3: Automatic scanning and data processing of extreme value method:

[0045] Python controls the output instructions of the PCI-6723 data acquisition card, driving the high-voltage power supply to increase the DC voltage in 5V steps starting from -600V. After the voltage stabilizes at each step, the PCI-6221 acquisition card acquires the power meter output voltage at a 10kHz sampling rate (for example, 2.15V is measured at -600V). A real-time sliding average algorithm (25-point window) is applied to suppress environmental vibration noise. After completing the full-range scan, the SciPy library is used to fit the quadratic function P(U) = aU 2 +bU+c, automatically mark the extreme point position (the maximum point U_max=+305V corresponds to P=8.92V, the minimum point U_min=-292V corresponds to P=1.07V), and calculate the half-wave voltage experimental value Uπ_exp1=|305-(-292)|=597V.

[0046] Step 4: Frequency doubling method synchronization verification stage:

[0047] During the DC voltage scanning process, frequency doubling verification is carried out synchronously: a 1kHz AC modulation signal is continuously applied, the 4-20mA current signal output by the photoelectric converter is collected in real time, and the spectrum distribution is calculated by fast Fourier transform (FFT); when it is detected that the amplitude of the 2kHz component accounts for more than 95% (for example, when U = -290V, the amplitude of the 2kHz component A_2f0 = 28.7dB, and the fundamental frequency component A_f0 = 0.3dB), it is determined to be a frequency doubling distortion point; the voltages of adjacent distortion points are continuously recorded (the first distortion point U_dist1 = -290V, the second distortion point U_dist2 = +308V), and the half-wave voltage verification value Uπ_exp2 = |308-(-290)| = 598V is calculated.

[0048] Step 5: Dual-algorithm cross-verification phase:

[0049] Comparing the results of the extreme value method and the frequency doubling method: the relative deviation δ is calculated as |597-598| / 597=0.17%. When δ is less than the 1% threshold, the data is considered valid. If δ is greater than 1% (such as due to temperature drift), the voltage step value is automatically reduced to 0.5V, and the ±50V range is expanded on both sides with the first distortion point as the center, and the collected data is rescanned until the deviation meets the requirements.

[0050] Step 6: Multi-wavelength expansion measurement phase:

[0051] The tunable laser source is controlled to output 560nm, 600nm, 650nm, 700nm, and 750nm wavelength beams in sequence; the extreme value method scanning and frequency doubling method verification are repeatedly performed at each wavelength, and the average of the two methods is taken as the final experimental value (for example, Uπ_avg = 597.5V at a wavelength of 650nm); based on the theoretical formula Uπ_theory = (λi×d) / (2×n0 3 ×γ 22 × L) calculated theoretical value (λi=650×10 -9 m, d = 0.003m, L = 0.035m); relative error δ = |597.5-598.3| / 598.3×100% = 0.13%.

[0052] Step 7: Temperature drift compensation and report generation stage:

[0053] The ambient temperature T (for example, T = 26.5°C) is monitored in real time using the AD590 temperature sensor, and the voltage baseline is corrected according to the compensation formula ΔU_comp = 0.1×(T-25) = +0.15V. The multi-wavelength half-wave voltage curve (wavelength λi on the horizontal axis, Uπ on the vertical axis) and the error distribution histogram are dynamically displayed on the Grafana platform. Finally, the Jinja2 template engine is called to generate a PDF format experimental report, which includes an experimental parameter table, an UP fitting curve (with extreme points marked), a wavelength-half-wave voltage relationship diagram (with superimposed theoretical / experimental curves), and a recommended optimal operating wavelength range (560-600nm) with δ < 0.5%.

[0054] Finally, a few points should be explained: First, in the description of this application, it should be noted that, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense, and may refer to mechanical or electrical connections, internal communication between two components, or direct connection. "Up," "down," "left," and "right" are only used to indicate relative positional relationships. When the absolute positions of the objects being described change, the relative positional relationships may also change.

[0055] Secondly: The drawings of the embodiments disclosed in the present invention only involve structures related to the embodiments disclosed in the present invention. Other structures may refer to conventional designs. The same embodiment and different embodiments of the present invention may be combined with each other without conflict.

[0056] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for automatically detecting half-wave voltage in a crystal modulation experiment, characterized in that include: A tunable laser source with a continuously adjustable wavelength of 560nm to 750nm is constructed, and the output beam is irradiated onto a lithium niobate crystal. A crystal modulation module is configured, and a DC bias voltage in the range of -1200V to 1200V and a step accuracy of 0.1V is applied through a high-voltage DC power supply. At the same time, a sinusoidal AC modulation signal with a frequency of 1Hz-10kHz and an adjustable amplitude of 0-50V is superimposed by the signal source. An integrated detection module is set up, and an optical power meter and a photoelectric converter are integrated on a horizontally adjustable bracket. The optical power meter converts the optical signal into a 0-10V analog voltage with an output accuracy of 0.1V, and the photoelectric converter converts the modulated optical signal into a 4-20mA analog current. By rotating the bracket, one of the two is precisely aligned with the center of the crystal output light, with an alignment error of no more than 0.1mm. An automated control process is executed, with the detection signal collected by the first data acquisition card and the control command output by the second data acquisition card. Parameter setting, data acquisition and dual-algorithm synchronous measurement are implemented based on a Python program, where the dual algorithm includes the extreme value method and the frequency doubling method.

2. The method for automatically detecting half-wave voltage in a crystal modulation experiment according to claim 1, characterized in that: The rotation mechanism of the horizontally adjustable bracket adopts precision thread transmission, and the single rotation angle is controlled within the range of ±5°, so that the center of the power meter's photosensitive surface or the photoelectric converter's receiving surface coincides with the crystal's output optical axis, and the light spot center offset is ≤0.1mm. No optical recalibration is required after switching; when the bracket rotates, the position sensor is automatically triggered and a currently activated detection unit type signal is sent to the control module.

3. The method for automatically detecting half-wave voltage in a crystal modulation experiment according to claim 1, characterized in that: The specific measurement process of the extreme value method is as follows: setting the DC voltage scanning range and step value, applying DC voltage point by point according to the step value and synchronously collecting optical power data; using a sliding average algorithm to process the voltage-power curve, the algorithm window width W and the voltage step value ΔV satisfy W = 5ΔV to maintain a 0.1V resolution; automatically fitting a quadratic curve function to locate adjacent maximum points U_max and minimum points U_min, and calculating the half-wave voltage experimental value Uπ = |U_max-U_min|.

4. The method for automatically detecting half-wave voltage in a crystal modulation experiment according to claim 1, characterized in that: The specific verification process of the frequency doubling method is as follows: continuously apply an AC modulation signal with a fixed frequency f0, and collect the spectrum of the output current signal of the photoelectric converter in real time; when it is detected that the amplitude of the frequency f0 component drops below 5% of the baseline and the amplitude of the frequency 2f0 component accounts for more than 95%, it is determined that frequency doubling distortion has occurred and the current DC voltage value U_dist is recorded; after continuously identifying two adjacent distortion points, the half-wave voltage verification value Uπ'=|U_dist1-U_dist2| is calculated.

5. The method and device for automatically detecting half-wave voltage in a crystal modulation experiment according to claims 3 and 4, characterized in that: A dual-algorithm collaborative mechanism is established. During the DC voltage scanning process, when the frequency doubling method detects the first distortion point U_dist, the extreme value method data resampling is immediately performed by expanding the step value range 10 times to both sides with U_dist as the center. If |Uπ-Uπ'| / Uπ>1%, the photoelectric converter gain is automatically adjusted and the voltage range is rescanned.

6. The method for automatically detecting half-wave voltage in a crystal modulation experiment according to claim 1, characterized in that: Perform multi-wavelength half-wave voltage relationship analysis, set more than 5 groups of different wavelengths λi (i = 1, 2, ..., n) in sequence, and complete dual algorithm measurement at each wavelength group; calculate the theoretical half-wave voltage value based on the physical parameters of the lithium niobate crystal, where λi is the laser vacuum wavelength (unit: meter), d = 3×10 -3 m is the thickness of the crystal along the electric field direction, L = 35 × 10 -3 m is the optical length of the crystal, n0=2.286 is the zero-field refractive index, γ 22 =6.8×10 -12 m / V is the electro-optic coefficient, and the theoretical formula is: Uπ theoretical value = (λi×d) / (2×n0 3 ×γ 22 ×L), generate the experimental wavelength-half-wave voltage curve, mark the relative error δ = |Uπ experimental value - Uπ theoretical value| / Uπ theoretical value × 100% at each point, and output the error distribution report.

7. The method for automatically detecting half-wave voltage in a crystal modulation experiment according to claim 1, characterized in that: The Python program includes a dynamic monitoring module that displays three core data items in real time: a DC voltage scan progress bar, an optical power versus voltage curve, and a photoelectric signal spectrum waterfall diagram. When the power meter output voltage is detected to be greater than 9.5V or the signal-to-noise ratio of the doubled frequency signal is less than 20dB, the program automatically pauses the measurement and triggers an audible and visual alarm. A built-in temperature drift compensation algorithm corrects baseline drift caused by ambient temperature based on historical data.

8. A computer-readable storage medium storing executable program code, characterized in that: The code is executed to realize the complete process described in claims 1-7, including: laser wavelength parameter configuration interface, detection unit switching control instructions, dual algorithm synchronous measurement thread, crystal physical parameter database (storage d, L, n0, γ 22 constants), multi-wavelength data comparison matrix and error analysis report generation module.

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