AlGaN solar blind APD test hardware design method and device

Abstract

The invention provides an AlGaN solar blind APD test hardware design method and device, and relates to the technical field of semiconductor testing, and the method comprises the steps: taking a signal state-amplification characteristic as a reference, carrying out the sample driving training, and constructing a self-adaptive amplification unit; a trans-impedance amplifier and a bias voltage adjusting unit are introduced, and a test circuit topology is obtained; an APD test current is obtained, amplification and parameter adjustment are carried out, signal amplification and current-voltage conversion are carried out in response to the trans-impedance amplifier, and a test voltage signal is output; and based on the bias voltage adjustment unit, reverse bias voltage adjustment is performed on the test voltage signal, and test data is determined and displayed at a PC end. According to the application, the technical problem that the test result is unstable due to the fact that the current signal output by the APD is usually very weak and is easily influenced by external noise in the prior art can be solved, and the stability of the test result is improved through the cooperative work of the adaptive amplification unit, the trans-impedance amplifier and the bias voltage adjustment unit.

Description

Technical Field

[0001] This application relates to the field of semiconductor testing technology, and particularly to a method and device for designing the hardware of an AlGaN solar-blind APD test. Background Art

[0002] An AlGaN solar-blind avalanche photodiode (APD) is a highly sensitive photodetector, which is widely used in the fields of photodetection under low light conditions, remote sensing, communication, imaging systems, etc. The APD has high gain and sensitivity, but at the same time faces many challenges, especially in weak signal detection and noise suppression. In order to convert the weak current signal output by the APD into a voltage signal that is easy to process, the existing test methods usually use a transimpedance amplifier (TIA) as the amplification unit to convert the weak current signal into a voltage signal. In addition, filters and gain adjustment functions are also equipped to improve the detectability of the signal. However, the current signal output by the APD is usually very weak and is often affected by environmental noise (such as electromagnetic interference, temperature changes, etc.). The existing amplification circuits are difficult to effectively amplify weak signals without introducing additional noise, resulting in a low signal-to-noise ratio and affecting the test accuracy and stability. In the process of increasing the APD bias voltage to improve the gain, although the signal intensity can be increased, the noise will also increase accordingly, resulting in a decrease in signal quality.

[0003] In summary, there is a technical problem in the prior art that the current signal output by the APD is usually very weak, is easily affected by external noise, and the signal distortion occurs, resulting in unstable test results. Summary of the Invention

[0004] The purpose of this application is to provide a method and device for designing the hardware of an AlGaN solar-blind APD test, so as to solve the technical problem in the prior art that the current signal output by the APD is usually very weak, is easily affected by external noise, and the signal distortion occurs, resulting in unstable test results.

[0005] In view of the above problems, this application provides a method and device for designing the hardware of an AlGaN solar-blind APD test.

[0006] In a first aspect, the present application provides a method for designing a hardware for testing AlGaN solar-blind APD. The method for designing a hardware for testing AlGaN solar-blind APD is implemented through a device for designing a hardware for testing AlGaN solar-blind APD. Among them, the method for designing a hardware for testing AlGaN solar-blind APD includes: taking the signal state-amplification characteristics as a benchmark, performing sample-driven training to construct an adaptive amplification unit, where the signal state is determined based on at least the signal intensity and environmental noise, and the amplification characteristics at least include gain characteristics and filtering characteristics; introducing a transimpedance amplifier and a bias adjustment unit to obtain the test circuit topology, where one end of the transimpedance amplifier is connected to the APD and the other end is connected to the bias adjustment unit, and the adaptive amplification unit automatically adjusts the parameters of the transimpedance amplifier; according to the test circuit topology, obtaining the APD test current, performing amplification parameter adjustment based on the adaptive amplification unit, in response to the transimpedance amplifier, performing signal amplification and current-voltage conversion, and outputting a test voltage signal; based on the bias adjustment unit, performing reverse bias adjustment on the test voltage signal, determining the test data and displaying it on the PC side.

[0007] In a second aspect, the present application further provides a device for designing a hardware for testing AlGaN solar-blind APD, which is used to execute the method for designing a hardware for testing AlGaN solar-blind APD as described in the first aspect. Among them, the device for designing a hardware for testing AlGaN solar-blind APD includes: an amplification unit construction module, which is used to take the signal state-amplification characteristics as a benchmark, perform sample-driven training to construct an adaptive amplification unit, where the signal state is determined based on at least the signal intensity and environmental noise, and the amplification characteristics at least include gain characteristics and filtering characteristics; a test circuit acquisition module, which is used to introduce a transimpedance amplifier and a bias adjustment unit to obtain the test circuit topology, where one end of the transimpedance amplifier is connected to the APD and the other end is connected to the bias adjustment unit, and the adaptive amplification unit automatically adjusts the parameters of the transimpedance amplifier; an amplification parameter adjustment module, which is used to obtain the APD test current according to the test circuit topology, perform amplification parameter adjustment based on the adaptive amplification unit, in response to the transimpedance amplifier, perform signal amplification and current-voltage conversion, and output a test voltage signal; a bias adjustment module, which is used to perform reverse bias adjustment on the test voltage signal based on the bias adjustment unit, determine the test data and display it on the PC side.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0009] By performing sample-driven training based on signal state-amplification characteristics, an adaptive amplification unit is constructed, where the signal state is determined based on at least signal intensity and environmental noise, and the amplification characteristics include at least gain characteristics and filtering characteristics; a transimpedance amplifier and a bias adjustment unit are introduced to obtain a test circuit topology, where one end of the transimpedance amplifier is connected to the APD and the other end is connected to the bias adjustment unit, and the adaptive amplification unit automatically adjusts the parameters of the transimpedance amplifier; according to the test circuit topology, the APD test current is obtained, and amplification parameter adjustment is performed based on the adaptive amplification unit. In response to the transimpedance amplifier, signal amplification and current-voltage conversion are performed to output a test voltage signal; based on the bias adjustment unit, reverse bias adjustment is performed on the test voltage signal to determine test data and display it on the PC side. That is to say, by constructing an adaptive amplification unit, the gain and filtering characteristics are automatically adjusted according to the signal intensity and environmental noise, a transimpedance amplifier and a bias adjustment unit are introduced, the weak current signal of the APD is converted into a voltage signal, and the working state of the APD is precisely controlled to balance gain and noise, thereby improving the signal output quality of the APD and the stability of the test results.

[0010] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically illustrates the specific embodiments of the present application. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. Brief Description of the Drawings

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings described below are only exemplary, and for those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0012] Figure 1 It is a schematic flow chart of a method for designing a hardware for testing an AlGaN solar-blind APD of the present application;

[0013] Figure 2 It is a schematic structural diagram of a device for designing a hardware for testing an AlGaN solar-blind APD of the present application.

[0014] Description of the reference numerals: Amplification unit construction module 11, Test circuit acquisition module 12, Amplification parameter adjustment module 13, Bias adjustment module 14. Detailed Description of the Embodiments

[0015] By providing a hardware design method and device for AlGaN solar-blind APD testing, the present application solves the technical problem in the prior art that since the current signal output by the APD is usually very weak and is easily affected by external noise, resulting in signal distortion, the test results are unstable. By constructing an adaptive amplification unit, the gain and filtering characteristics are automatically adjusted according to the signal strength and environmental noise. A transimpedance amplifier and a bias adjustment unit are introduced to convert the weak current signal of the APD into a voltage signal and precisely control the working state of the APD to balance the gain and noise, thereby improving the signal output quality of the APD and the stability of the test results.

[0016] Next, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the example embodiments described herein. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application. Additionally, it should be noted that for the sake of description, only the parts related to the present application are shown in the drawings rather than all of them.

[0017] Example 1, please refer to the attached Figure 1 The present application provides a hardware design method for AlGaN solar-blind APD testing. Among them, the hardware design method for AlGaN solar-blind APD testing is applied to a hardware design device for AlGaN solar-blind APD testing. The hardware design method for AlGaN solar-blind APD testing specifically includes the following steps:

[0018] S100: Based on the signal state-amplification characteristics as a reference, perform sample-driven training to construct an adaptive amplification unit. Among them, the signal state is at least determined based on the signal strength and environmental noise, and the amplification characteristics at least include gain characteristics and filtering characteristics.

[0019] Specifically, the signal state refers to the characteristics of the signal and environmental conditions, mainly including the signal strength, frequency, noise level, etc. The signal state determines the quality of the signal and the strategy required for amplification. In the APD test hardware, the signal state is usually jointly determined by the current intensity output by the APD and the environmental noise level. In APD testing, the signal strength is usually very weak, ranging from picoamperes (pA) to nanoamperes (nA). It is not feasible to directly measure the signal. In order to achieve effective and reliable measurement of weak current signals, it is necessary to use an amplification circuit and a filtering circuit to process and amplify the current signal to improve the signal-to-noise ratio and ensure that the signal can be accurately captured by the subsequent processing system.

[0020] The amplification characteristics refer to the key parameters of a transimpedance amplifier, mainly including the gain characteristic and the filtering characteristic, which directly affect the amplification effect and signal quality of the amplifier. The gain characteristic refers to the degree of amplification of the transimpedance amplifier for a signal. Gain is a key parameter of the transimpedance amplifier and determines the ratio between the input signal and the output signal. Excessive gain may amplify noise, while too low gain may not be able to effectively amplify weak signals. The filtering characteristic refers to the property of the transimpedance amplifier used to reduce or remove noise. The filter helps to remove unwanted frequency components and make the signal cleaner.

[0021] The signal state is determined by the signal strength and ambient noise. Different signal strengths and noise levels require different amplification characteristics. Collect a set of data samples containing different signal states and corresponding amplification characteristics. Based on the signal state - amplification characteristic as a benchmark, construct an adaptive amplification unit through sample - driven training. The training samples consist of multiple signal states and corresponding amplification characteristics. By training on the amplification samples, the adaptive amplification unit learns how to select the most appropriate gain and filtering strategies under specific signal states.

[0022] Sample - driven training will input information such as signal strength and noise level, and output corresponding amplification strategies (gain, filter settings) according to these input data. Through multiple trainings, the model can obtain an adaptive amplification unit, enabling it to automatically adjust the parameters of the transimpedance amplifier according to the signal state during actual testing, thereby optimizing the amplification effect of the signal. For example, when the output signal of the APD is weak, automatically select a higher gain and strengthen filtering to ensure that the signal is not masked by noise; when the signal strength is high, appropriately reduce the gain to avoid over - amplifying noise. The adaptive amplification unit can automatically adjust the gain and filtering strategies according to different signal states and noise environments, greatly improving the adaptability of the APD in complex environments. Through adaptive adjustment, amplify more when the signal strength is weak, and avoid over - amplification when the signal strength is strong, ensuring signal quality while reducing noise interference.

[0023] S200: Introduce a transimpedance amplifier and a bias adjustment unit to obtain the test circuit topology. Among them, one end of the transimpedance amplifier is connected to the APD, and the other end is connected to the bias adjustment unit. The adaptive amplification unit performs automatic parameter adjustment on the transimpedance amplifier.

[0024] Specifically, a transimpedance amplifier is an amplifier that converts a current signal into a voltage signal and is commonly used to measure current signals. By combining the input current with a feedback resistor, it outputs a voltage signal proportional to the input current. In the APD test hardware, the transimpedance amplifier is used to amplify the weak current signal output by the APD into a voltage signal. The bias voltage adjustment unit is used to adjust and control the bias voltage (i.e., the operating voltage) of the APD to ensure that the APD operates at the most suitable operating point. The adjustment of the bias voltage is crucial for the performance of the APD (such as response speed, sensitivity, etc.), so precise bias voltage adjustment helps to improve the accuracy and stability of the test.

[0025] In the APD test hardware, the transimpedance amplifier and the bias voltage adjustment unit are introduced to ensure that the weak current signal of the APD can be effectively converted into a voltage signal and tested at an appropriate operating voltage. The input end of the transimpedance amplifier can be directly connected to the output end of the APD, and the other port of the transimpedance amplifier is connected to the bias voltage adjustment unit. The current output of the APD is converted into a corresponding voltage output after passing through the transimpedance amplifier, and this voltage signal can then be collected by the ADC.

[0026] The test circuit topology refers to the connection method and layout of each electronic component (such as transimpedance amplifier, APD, bias voltage adjustment unit, etc.) in the test hardware, which determines how signals flow, how they are converted, and how effective measurement and control are carried out. One end of the transimpedance amplifier is connected to the APD, and the other end is connected to the ground or power supply through the bias voltage adjustment unit. The current signal of the APD is converted into a voltage signal by the transimpedance amplifier, and the operating state of the APD is adjusted through the bias voltage adjustment unit. The role of the adaptive amplification unit is to automatically adjust the parameters of the transimpedance amplifier according to real-time signal characteristics (such as signal strength, noise level), and optimize the amplification effect of the signal in real time according to different test conditions.

[0027] S300: According to the test circuit topology, obtain the APD test current, perform amplification parameter adjustment based on the adaptive amplification unit, and in response to the transimpedance amplifier, perform signal amplification and current-voltage conversion, and output a test voltage signal.

[0028] Specifically, according to the test circuit topology, obtain the APD test current, that is, the current signal output from the APD (avalanche photodiode). The test circuit topology ensures an effective connection between the output end of the APD and the input end of the transimpedance amplifier. Obtain the real-time state (such as signal amplitude and noise level) of the APD test current based on the APD test current, transmit the real-time state to the adaptive amplification unit, trigger N amplification units to make parallel amplification decisions, perform amplification processing on the same signal with different parameters, so as to compare and select the optimal amplification characteristics. Select the amplification characteristic with the best amplification effect from multiple amplification characteristics as the signal amplification characteristic.

[0029] Transmit the signal amplification feature to the transimpedance amplifier and dynamically adjust the feedback resistance value of the transimpedance amplifier. Perform signal amplification processing on the APD test current and simultaneously complete the conversion from current to voltage. Amplify the weak current through the transimpedance amplifier and achieve high-precision conversion from current to voltage through the feedback resistor in the transimpedance amplifier. The test voltage signal refers to the voltage signal finally output by the transimpedance amplifier, which is the result of the amplification and conversion of the current signal and is used for subsequent analog-to-digital conversion and data processing. Throughout the entire process from the APD test current to the test voltage signal, the signal characteristics are completely preserved. The parameters of the transimpedance amplifier are adjusted in real time through the adaptive amplification unit to effectively cope with the dynamic changes of the test signal, effectively process weak signals, and the stability and accuracy of the output voltage signal both meet the test requirements of the APD.

[0030] S400: Based on the bias voltage adjustment unit, perform reverse bias voltage adjustment on the test voltage signal, determine the test data, and display it on the PC side.

[0031] Specifically, use the ADC to collect voltage data and perform analog-to-digital conversion on the test voltage signal, quantize it into a digital signal according to a certain resolution and sampling rate, convert the voltage signal output by the transimpedance amplifier into a digital signal that can be processed by the MCU for computer processing and analysis. The digital voltage signal determines the bias voltage adjustment range through an algorithm to ensure that the bias voltage is constrained near the balance point. The bias voltage adjustment unit controls the reverse bias circuit to dynamically adjust the bias voltage and determine the test data, including key performance indicators such as gain, voltage signal amplitude, signal-to-noise ratio, etc. Transmit the test data to the PC through a data interface (such as serial communication, Ethernet, or other communication interfaces) and a communication protocol (such as TCP / IP) for data visualization on the PC side and display it in the form of charts or real-time curves. By dynamically adjusting the reverse bias voltage, ensure the stability and high-quality amplification effect of the test signal, effectively optimize the signal-to-noise ratio, and achieve weak signal detection without introducing excessive noise.

[0032] Furthermore, S100 of this application includes:

[0033] Interact with the amplification mechanism of the transimpedance amplifier; based on the amplification mechanism, perform big data retrieval and call amplification samples, where the amplification samples include sample signal states - sample amplification strategies; according to the amplification samples, perform sample supervised training until convergence to obtain the adaptive amplification unit.

[0034] Specifically, a transimpedance amplifier is an electronic component commonly used to convert a weak current signal into a voltage signal. Its main advantages include providing high gain and low noise, and it is particularly suitable for processing the current signal output by an APD because it can effectively convert the weak signal into a voltage form that is easier to measure, thus ensuring the detectability of the signal without being masked by noise. When the input current signal enters the transimpedance amplifier, the amplifier converts it into a voltage signal proportional to the input current through an internal resistor (referred to as the transimpedance). The transimpedance amplifier converts the weak input current into a voltage output through a feedback resistor. Since the APD (avalanche photodiode) outputs a weak current, the transimpedance amplifier can effectively increase the signal strength during this process, making it easier to further process.

[0035] The amplification mechanism refers to the physical principle and operation process of how a transimpedance amplifier works, including its gain setting, noise performance, etc. There may be differences under different specifications and types. Determine homologous samples to avoid the influence of irrelevant sample data on model supervised training. The gain and amplification effect of the transimpedance amplifier are affected by various factors (such as noise, signal strength, temperature, etc.). By retrieving and calling amplification samples through big data, an appropriate gain strategy is selected. Big data retrieval mainly obtains corresponding data by searching databases or known signal sample sets, which includes the amplification effects and their strategies of signals under different environmental conditions.

[0036] The amplification samples include paired data of signal states and amplification strategies. The signal state refers to information such as the strength and noise level of the signal, and the amplification strategy refers to how to adjust parameters such as the gain and filtering of the transimpedance amplifier to optimize the signal quality. According to different signal states, the amplification samples will indicate the optimal gain, bias voltage, or filtering configuration. Supervised training refers to training a machine learning model through known inputs and outputs. By gradually learning from the sample data, the model can accurately output the corresponding amplification strategy according to the input signal state, making the prediction result (amplification strategy) of the model as close as possible to the true value.

[0037] According to the amplification samples, multiple amplification units are supervised and trained. Each amplification unit selects the corresponding amplification strategy according to the signal state of its specific training set and adjusts its parameters. The training process will continue until the prediction result of the model converges, that is, the model can accurately output the corresponding amplification strategy in any signal state. Integrate multiple amplification units to obtain an adaptive amplification unit, which dynamically adjusts parameters such as gain and filtering frequency according to the state of the real-time input signal (such as strength, noise, etc.) to ensure that the signal can be amplified in an optimal way. Through supervised training, the adaptive amplification unit can automatically select the most suitable amplification strategy in different signal environments and improve the amplification effect of the signal.

[0038] Furthermore, the present application also includes the following steps:

[0039] Traverse the amplified samples, randomly extract a preset number of first samples, and supervise the training of the first amplification unit; traverse the amplified samples, randomly extract a preset number of second samples, and supervise the training of the second amplification unit; wherein, there is sample overlap between the first sample and the second sample; until the training of the Nth amplification unit is completed, integrate the first amplification unit, the second amplification unit until the Nth amplification unit to generate the adaptive amplification unit.

[0040] Specifically, traversing the amplified samples involves different signal states and corresponding amplification strategies. Randomly extract a certain number of first samples from them and use the first samples to supervise the training of the first amplification unit. By providing labeled data (i.e., the pairing of signal states and amplification strategies), learn how to adjust amplification parameters such as gain according to the signal state. Take the extracted signal state as the input and the amplification strategy as the output, and sequentially adjust the internal parameters (such as gain, filtering settings, etc.) of the first amplification unit. The purpose of training is to enable the amplification unit to accurately optimize according to the characteristics of the input signal. The first amplification unit will gradually learn how to adjust its amplification strategy according to the signal state, so that it can output the most appropriate amplification result under similar environmental conditions. The preset number is the number of samples specified during training, usually defined according to the actual situation and specific requirements, which helps to maintain the randomness of training and avoid over-reliance on certain specific samples during the training process.

[0041] Select a suitable machine learning model, such as linear regression, neural network, etc. Divide the first samples into a dataset and a validation set, usually 80% for the training set and 20% for the validation set. Use the training set to train the model and continuously try to find the relationship between the input signal state and the amplification parameters. Regularly use the validation dataset to evaluate the performance of the model, which helps to monitor whether the model is overfitting or underfitting and adjust the training strategy. According to the results of the performance evaluation, adjust the parameters of the model, such as the learning rate, number of layers, number of neurons, etc., to optimize the performance of the model. Repeat the training and evaluation process until the model reaches satisfactory performance.

[0042] Similarly, randomly extract a certain number of second samples from the amplified samples and use the second samples to supervise the training of the second amplification unit. There will be some identical samples in the first samples and the second samples. Especially when the data volume is large, data overlap ensures the diversity of the training process and at the same time avoids overfitting of the model to a certain type of data. Different from the first amplification unit, the training data of the second amplification unit may partially overlap with the training data of the first amplification unit, which helps the model to generalize better and avoid overfitting. During the training process, the second amplification unit will also adjust the corresponding gain and filtering strategies according to the input signal state.

[0043] Perform similar operations continuously to train the Nth amplification unit in sequence. During the training process, each amplification unit optimizes its response manner under different signal states by continuously and randomly extracting samples and adjusting parameters, ensuring that each amplification unit can independently provide a suitable amplification strategy for a specific signal state. Integrate the trained first amplification unit, second amplification unit up to the Nth amplification unit, and jointly form an adaptive amplification unit through a certain mechanism (such as weighted selection, etc.). The final adaptive amplification unit can make parallel amplification decisions through N amplification units according to different states of the input signal, and automatically select a suitable amplification strategy from them. Judge to activate all amplification units under different signal states according to the selection mechanism of the input signal characteristics, and select the most suitable amplification unit according to the input signal characteristics (such as signal strength, noise level, etc.), that is, the amplification unit with the best signal effect. By training multiple amplification units, the gain and filtering strategies are flexibly adjusted according to different states of the input signal, and each amplification unit is optimized according to the signal state of its specific training set, greatly improving the adaptability to different environmental conditions.

[0044] Further, step S300 of the present application includes:

[0045] For the APD test current, determine the test signal state; transmit the test signal state to the adaptive amplification unit, trigger the first amplification unit, the second amplification unit up to the Nth amplification unit to make parallel amplification decisions, and determine N amplification characteristics; traverse the N amplification characteristics, and select the item with the largest proportion as the signal amplification characteristic.

[0046] Specifically, analyze the APD test current to determine the test signal state, that is, the current characteristics and attributes of the signal, such as signal strength and environmental noise level, etc. Transmit the determined test signal state to the adaptive amplification unit, and multiple amplification units (such as the first amplification unit, the second amplification unit, etc.) of the adaptive amplification unit start parallel amplification decisions based on this signal state. Each amplification unit performs amplification processing with different parameters according to the signal state to determine N amplification characteristics, including different gain settings and filtering parameters, to adapt to different signal conditions.

[0047] The adaptive amplification unit is a module composed of multiple amplification units (such as the first amplification unit, the second amplification unit, etc.), and is used to automatically adjust the amplification parameters. Through parallel processing and amplification strategy optimization, it can dynamically select the best signal amplification scheme. Parallel amplification decision means that multiple amplification units (such as N amplification units) are activated simultaneously to perform amplification processing on the same signal with different parameters, so as to compare and select the optimal amplification characteristics. After each unit processes the signal independently, it outputs the corresponding amplification characteristics, such as signal gain, signal-to-noise ratio.

[0048] Traverse the N amplified features, compare the N amplified features (such as gain, signal-to-noise ratio), compare the signal amplification effect of each amplified feature, and select the one with the best signal effect as the signal amplification feature. The maximum ratio term refers to the feature that contributes the most to signal amplification among multiple amplified features. By combining the test signal status and the parallel amplification decision, the adaptive amplification unit can select the optimal amplification strategy in real time to adapt to different signal characteristics.

[0049] Furthermore, the present application further includes the following steps:

[0050] Transmit the signal amplification feature to the transimpedance amplifier; control the transimpedance amplifier to perform signal amplification processing on the APD test current to determine the amplified current signal; perform current-voltage conversion on the amplified current signal to determine the test voltage signal.

[0051] Specifically, transmitting the selected signal amplification feature to the transimpedance amplifier includes gain (the multiple of signal amplification), signal-to-noise ratio (the ratio of signal to noise), and frequency response, etc. The transimpedance amplifier adjusts its parameters according to the signal amplification feature, performs signal amplification processing on the APD test current, and optimally amplifies the weak current signal of the APD test current to determine the amplified current signal. The amplified current signal refers to the current signal after being processed by the transimpedance amplifier, which amplifies the weak APD output current.

[0052] Perform current-voltage conversion on the amplified current signal, and directly convert the amplified current signal into a voltage signal through the internal resistance element of the transimpedance amplifier. For example, using a highly stable feedback resistor and noise-resistant design (such as shielding the noise source and optimizing the PCB layout) to ensure conversion accuracy and signal integrity. By transmitting the signal amplification feature and adjusting the parameters of the transimpedance amplifier, it can dynamically adapt to signals of different intensities and frequencies.

[0053] Furthermore, S400 of the present application includes:

[0054] Based on the ADC acquisition unit, perform signal acquisition and analog-to-digital conversion on the test voltage signal to determine the digital voltage signal; transmit the digital voltage signal to the bias adjustment unit, and determine the test data through reverse bias adjustment; according to the data interface and communication protocol, transmit the test data to the PC for interface display.

[0055] Specifically, the ADC acquisition unit is an analog-to-digital conversion unit used to convert analog signals (test voltage signals) into digital signals. When the test voltage signal is output from the transimpedance amplifier, it is subsequently captured by the input terminal of the ADC acquisition unit. The ADC acquisition unit converts the voltage signal output by the transimpedance amplifier into a digital signal that can be processed by the MCU through signal acquisition. The performance of the ADC module directly affects the resolution and accuracy of data acquisition. For the AlGaN solar-blind APD test hardware, a suitable ADC chip needs to be selected and a reasonable acquisition mode configured. Analog-to-digital conversion is the process of discretizing analog signals (continuous values) into digital signals (discrete values). Digital voltage signals are usually in binary form, and the resolution of the conversion is determined by the number of bits of the ADC. For example, a 12-bit ADC can provide 4096 different voltage levels.

[0056] The digital voltage signal is transmitted to the bias adjustment unit. Under the constraint of the balance point, reverse bias adjustment is performed to adjust the reverse bias of the APD according to experimental requirements, thereby affecting its gain and response characteristics. If it is detected that the signal strength is insufficient or the noise is too high, a reverse bias is applied to the APD through the bias adjustment unit to fine-tune its operating point. The adjustment of the reverse bias can be an increase or a decrease, depending on the signal state and the required optimization direction. The output test data includes state parameters after bias adjustment, such as key parameters like gain, signal amplitude, and noise ratio.

[0057] The test data is transmitted to the PC using a communication interface (such as UART or Ethernet), and a communication protocol (such as RS-232, TCP / IP) is configured to ensure the stability and accuracy of data transmission. UART is a common serial communication protocol used for data interaction between the PC and embedded devices. The test data received at the PC end is presented in an intuitive form through the GUI (Graphical User Interface), such as curves, tables, etc., for easy user analysis. The bias adjustment unit responds in real-time to changes in the digital signal to ensure that the APD operates in the best state, enhancing test stability. Through a reliable communication protocol and interface, the test data is transmitted to the PC end with low latency and high integrity.

[0058] Furthermore, the present application further includes the following steps:

[0059] Determine the balance point between the gain and the signal-to-noise ratio. Among them, the APD gain is positively correlated with the increase in the bias voltage, and the APD gain is negatively correlated with the signal-to-noise ratio; according to the balance point, constrain the reverse bias adjustment of the test voltage signal.

[0060] Specifically, the gain refers to the amplification factor of an avalanche photodiode (APD) for an optical signal, which is determined by the bias voltage. As the bias voltage increases, the electric field strength inside the APD increases, thereby improving the optoelectronic conversion efficiency. The signal-to-noise ratio represents the ratio of the signal strength to the noise strength. A high signal-to-noise ratio indicates good signal quality and little influence of noise on the signal, and it is commonly expressed in decibels (dB). The APD gain increases exponentially with the bias voltage, but noise (such as avalanche noise and thermal noise) is also introduced during the amplification process. Excessive gain will increase the noise and reduce the signal-to-noise ratio; too low gain will result in insufficient signal strength and it is difficult to detect weak signals. Therefore, it is necessary to determine an optimal bias setting that can detect weak signals without introducing too much noise.

[0061] As the APD gain increases, although the signal is amplified, the noise is also amplified at the same time, which will lead to a decrease in the signal-to-noise ratio. APDs made of different materials generally have different requirements for the bias voltage. Some may only require dozens of volts, while others require up to several hundred volts. For the AlGaN solar-blind APD test hardware, the bias voltage generally needs to be set in the range of 5V to 75V. In order to achieve a higher output voltage, the APD bias voltage circuit design is generally based on a boost DC / DC conversion circuit. In order to further achieve a higher bias voltage, a diode voltage multiplier circuit can be added at the later stage to achieve a higher output voltage.

[0062] Adjust the bias voltage between 5V and 75V, record the APD gain and signal-to-noise ratio at each bias voltage, plot the curves of the gain and signal-to-noise ratio changing with the bias voltage, and find the intersection point or the optimal balance region of the gain curve and the signal-to-noise ratio curve. Usually, it is the point where the gain is high enough to detect weak signals while the signal-to-noise ratio still remains at a relatively high level. At the balance point, that is, the point where the best state is achieved between the gain and the signal-to-noise ratio, determine the optimal bias voltage, which can amplify weak signals without introducing too much noise.

[0063] According to the determined balance point, set the bias voltage of the APD and adjust the reverse bias of the test voltage signal. The adjustment of the reverse bias should be restricted by the balance point to avoid a decrease in the signal-to-noise ratio caused by excessive gain. If the test voltage signal deviates from the best balance point, the bias voltage adjustment unit gradually adjusts the bias voltage until the signal-to-noise ratio and the gain meet the preset requirements. By restricting the dynamic range of the bias voltage, the stability of the test voltage signal is ensured, and data fluctuations caused by too high or too low voltage are reduced.

[0064] In summary, the AlGaN solar-blind APD test hardware design method provided by this application has the following technical effects:

[0065] By taking the signal state-amplification feature as a reference, performing sample-driven training to construct an adaptive amplification unit, wherein the signal state is determined based on at least signal intensity and environmental noise, and the amplification feature at least includes a gain feature and a filtering feature; introducing a transimpedance amplifier and a bias adjustment unit to obtain a test circuit topology, wherein one end of the transimpedance amplifier is connected to the APD and the other end is connected to the bias adjustment unit, and the adaptive amplification unit performs automatic parameter adjustment on the transimpedance amplifier; according to the test circuit topology, obtaining the APD test current, performing amplification parameter adjustment based on the adaptive amplification unit, in response to the transimpedance amplifier, performing signal amplification and current-voltage conversion, and outputting a test voltage signal; based on the bias adjustment unit, performing reverse bias adjustment on the test voltage signal, determining test data and performing PC-side display. That is to say, by constructing an adaptive amplification unit, automatically adjusting the gain and filtering characteristics according to the signal intensity and environmental noise, introducing a transimpedance amplifier and a bias adjustment unit, converting the weak current signal of the APD into a voltage signal, and precisely controlling the working state of the APD to balance the gain and noise, thereby improving the signal output quality of the APD and the stability of the test results.

[0066] Embodiment 2. Based on the same inventive concept as the method for designing the hardware for testing an AlGaN solar-blind APD in the foregoing Embodiment 1, the present application further provides a device for designing the hardware for testing an AlGaN solar-blind APD. Please refer to the attached Figure 2 , the device for designing the hardware for testing an AlGaN solar-blind APD includes:

[0067] An amplification unit construction module 11, which is used to perform sample-driven training based on the signal state-amplification feature to construct an adaptive amplification unit, wherein the signal state is determined based on at least signal intensity and environmental noise, and the amplification feature at least includes a gain feature and a filtering feature; a test circuit acquisition module 12, which is used to introduce a transimpedance amplifier and a bias adjustment unit to obtain a test circuit topology, wherein one end of the transimpedance amplifier is connected to the APD and the other end is connected to the bias adjustment unit, and the adaptive amplification unit performs automatic parameter adjustment on the transimpedance amplifier; an amplification parameter adjustment module 13, which is used to obtain the APD test current according to the test circuit topology, perform amplification parameter adjustment based on the adaptive amplification unit, in response to the transimpedance amplifier, perform signal amplification and current-voltage conversion, and output a test voltage signal; a bias adjustment module 14, which is used to perform reverse bias adjustment on the test voltage signal based on the bias adjustment unit, determine test data and perform PC-side display.

[0068] Further, the building block 11 of the amplification unit in the AlGaN solar-blind APD test hardware design device is further configured to:

[0069] Interact with the amplification mechanism of the transimpedance amplifier; based on the amplification mechanism, perform big data retrieval and call amplification samples, where the amplification samples include sample signal states - sample amplification strategies; according to the amplification samples, perform sample supervised training until convergence to obtain the adaptive amplification unit.

[0070] Further, the building block 11 of the amplification unit in the AlGaN solar-blind APD test hardware design device is further configured to:

[0071] Traverse the amplification samples, randomly extract a preset number of first samples, and supervise the training of the first amplification unit; traverse the amplification samples, randomly extract a preset number of second samples, and supervise the training of the second amplification unit; where there is sample overlap between the first sample and the second sample; until the training of the Nth amplification unit is completed, integrate the first amplification unit, the second amplification unit until the Nth amplification unit to generate the adaptive amplification unit.

[0072] Further, the amplification parameter adjustment module 13 in the AlGaN solar-blind APD test hardware design device is further configured to:

[0073] For the APD test current, determine the test signal state; transmit the test signal state to the adaptive amplification unit, trigger the first amplification unit, the second amplification unit until the Nth amplification unit to perform parallel amplification decisions, and determine N amplification features; traverse the N amplification features, select the item with the largest proportion as the signal amplification feature.

[0074] Further, the amplification parameter adjustment module 13 in the AlGaN solar-blind APD test hardware design device is further configured to:

[0075] Transmit the signal amplification feature to the transimpedance amplifier; control the transimpedance amplifier to perform signal amplification processing on the APD test current to determine the amplified current signal; perform current-voltage conversion on the amplified current signal to determine the test voltage signal.

[0076] Further, the bias voltage adjustment module 14 in the AlGaN solar-blind APD test hardware design device is further configured to:

[0077] Based on the ADC acquisition unit, the test voltage signal is collected and analog-to-digital converted to determine the digital voltage signal; the digital voltage signal is transmitted to the bias voltage adjustment unit, and the test data is determined by performing reverse bias voltage adjustment; according to the data interface and communication protocol, the test data is transmitted to the PC side for interface display.

[0078] Further, the bias voltage adjustment module 14 in the above-mentioned AlGaN solar-blind APD test hardware design device is further used for:

[0079] Determine the balance point of the gain and the signal-to-noise ratio. Among them, the APD gain is positively correlated with the increase in the bias voltage, and the APD gain is negatively correlated with the signal-to-noise ratio; according to the balance point, the reverse bias voltage adjustment of the test voltage signal is constrained.

[0080] In the present specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The above-mentioned Figure 1 A method and specific example of an AlGaN solar-blind APD test hardware design in the first embodiment are equally applicable to the AlGaN solar-blind APD test hardware design device in this embodiment. Through the above detailed description of a method for designing an AlGaN solar-blind APD test hardware, those skilled in the art can clearly know the AlGaN solar-blind APD test hardware design device in this embodiment. Therefore, for the sake of simplicity of the specification, it will not be elaborated here. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0081] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0082] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is also intended to include these changes and variations.

Claims

1. A method for designing AlGaN solar-blind APD test hardware, characterized in that: include: Based on the signal state-amplification feature, sample-driven training is performed to construct an adaptive amplification unit, wherein the signal state is determined based on at least signal strength and environmental noise, and the amplification feature includes at least gain feature and filtering feature; A transimpedance amplifier and a bias adjustment unit are introduced to obtain a test circuit topology, wherein one end of the transimpedance amplifier is connected to the APD, and the other end is connected to the bias adjustment unit, and the adaptive amplification unit automatically adjusts the parameters of the transimpedance amplifier; According to the test circuit topology, an APD test current is obtained, amplification and parameter adjustment are performed based on the adaptive amplification unit, and in response to the transimpedance amplifier, signal amplification and current-voltage conversion are performed to output a test voltage signal; Based on the bias adjustment unit, the test voltage signal is reverse bias adjusted, and the test data is determined and displayed on the PC.

2. The AlGaN solar-blind APD test hardware design method according to claim 1, characterized in that: Perform sample-driven training and build an adaptive amplification unit, including: Interacting the amplification mechanism of the transimpedance amplifier; Based on the amplification mechanism, perform big data retrieval and call amplified samples, wherein the amplified samples include sample signal status-sample amplification strategy; According to the amplified samples, sample supervised training is performed until convergence to obtain the adaptive amplification unit.

3. The AlGaN solar-blind APD test hardware design method according to claim 2, characterized in that: According to the enlarged sample, sample supervised training is performed, including: Traversing the amplified samples, randomly extracting a preset number of first samples, and supervising the training of the first amplification unit; Traversing the amplified samples, randomly extracting a preset number of second samples, and supervising the training of the second amplification unit; wherein there is sample overlap between the first sample and the second sample; Until the training of the Nth amplifying unit is completed, the first amplifying unit, the second amplifying unit and the Nth amplifying unit are integrated to generate the adaptive amplifying unit.

4. The AlGaN solar-blind APD test hardware design method according to claim 3, characterized in that: Performing amplification parameter adjustment based on the adaptive amplification unit includes: Determining a test signal state for the APD test current; Transmitting the test signal state to the adaptive amplification unit, triggering the first amplification unit, the second amplification unit, and finally the Nth amplification unit to make parallel amplification decisions and determine N amplification features; The N amplification features are traversed, and the item with the largest proportion is selected as the signal amplification feature.

5. The AlGaN solar-blind APD test hardware design method according to claim 4, characterized in that: In response to the transimpedance amplifier, signal amplification and current-voltage conversion are performed, including: transmitting the signal amplification characteristic to the transimpedance amplifier; Controlling the transimpedance amplifier to perform signal amplification processing on the APD test current to determine an amplified current signal; The amplified current signal is converted from current to voltage to determine the test voltage signal.

6. The AlGaN solar-blind APD test hardware design method according to claim 1, characterized in that: Based on the bias adjustment unit, the test voltage signal is reverse bias adjusted, the test data is determined and displayed on the PC, including: Based on the ADC acquisition unit, the test voltage signal is acquired and analog-to-digital converted to determine a digital voltage signal; Transmitting the digital voltage signal to the bias adjustment unit, and determining test data by performing reverse bias adjustment; According to the data interface and communication protocol, the test data is transmitted to the PC for interface display.

7. The AlGaN solar-blind APD test hardware design method according to claim 6, characterized in that: Before the reverse bias adjustment is performed, the method includes: Determining a balance point between gain and signal-to-noise ratio, wherein the APD gain is positively correlated with an increase in bias voltage, and the APD gain is negatively correlated with the signal-to-noise ratio; According to the balance point, a reverse bias adjustment of the test voltage signal is constrained.

8. An AlGaN solar-blind APD test hardware design device, characterized in that: The method for designing AlGaN solar-blind APD test hardware according to any one of claims 1 to 7 is used, wherein the AlGaN solar-blind APD test hardware design device comprises: an amplification unit construction module, the amplification unit construction module is used to perform sample-driven training based on a signal state-amplification feature to construct an adaptive amplification unit, wherein the signal state is determined based on at least signal strength and environmental noise, and the amplification feature includes at least a gain feature and a filtering feature; A test circuit acquisition module, the test circuit acquisition module is used to introduce a transimpedance amplifier and a bias adjustment unit to obtain a test circuit topology, wherein one end of the transimpedance amplifier is connected to the APD and the other end is connected to the bias adjustment unit, and the adaptive amplification unit automatically adjusts the parameters of the transimpedance amplifier; an amplification and parameter adjustment module, the amplification and parameter adjustment module being used to obtain the APD test current according to the test circuit topology, perform amplification and parameter adjustment based on the adaptive amplification unit, perform signal amplification and current-voltage conversion in response to the transimpedance amplifier, and output a test voltage signal; A bias adjustment module is used to perform reverse bias adjustment on the test voltage signal based on the bias adjustment unit, determine test data and display it on the PC.

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