A rapid noise traction method and device

Abstract

This invention discloses a rapid noise pulling method and apparatus, belonging to the field of chip measurement technology. The method includes: pre-calibrating a noise pulling system using a power meter, and then using the calibrated noise pulling system to perform the noise pulling process; the noise pulling system does not include a noise source; after completing S-parameter calibration, deriving the relationship between the system noise figure and the source reflection coefficient using the noise figure as a function of the noise figure and the power measurement results; based on the relationship between the system noise figure and the noise figure, extracting the noise parameters of the device under test using noise pulling technology with variable input impedance. This invention can improve the testing speed and accuracy during the noise pulling process.

Description

Technical Field

[0001] This invention relates to the field of chip measurement technology, and in particular to a rapid noise-pulling method and apparatus. Background Technology

[0002] Noise parameters, as a key performance indicator of the receiving channel in a wireless transceiver system, are quality factors used to characterize the amount of excessive noise in a system and are one of the core indicators in the field of high-frequency electronics. The noise figure of transistors, amplifiers, etc., varies with the source impedance. To achieve the minimum noise figure, the source impedance is usually not at 50Ω. Therefore, the key to circuit design is to use a matching network to transform 50Ω into the optimal noise impedance to obtain the minimum noise figure. To obtain the noise figure values ​​under different source impedances and to obtain various noise parameters, noise pulling techniques are required.

[0003] Noise-driven methods in related technologies mainly include fixed-frequency sweeping and fixed-point sweeping. However, there are still some issues with testing speed and accuracy. Summary of the Invention

[0004] This invention provides a rapid noise extraction method and apparatus, which can solve the problems of poor testing speed and accuracy in related technologies. The technical solution is as follows:

[0005] On the one hand, a rapid noise traction method is provided, the method comprising:

[0006] The noise traction system is pre-calibrated using a power meter to perform the noise traction process using the calibrated noise traction system; the noise traction system does not include a noise source.

[0007] After completing the S-parameter calibration, the relationship between the system noise figure and the source reflection coefficient is derived using the noise figure as a function of the source reflection coefficient and the power measurement results.

[0008] Based on the relationship of the system noise figure, the noise parameters of the device under test are extracted using noise traction technology with variable input impedance.

[0009] On the other hand, a rapid noise traction device is provided, the device comprising:

[0010] A calibration unit is used to pre-calibrate the noise traction system using a power meter, so as to perform the noise traction process using the calibrated noise traction system; the noise traction system does not include a noise source;

[0011] The processing unit is used to derive the relationship between the system noise figure and the source reflection coefficient after the S-parameter calibration is completed, using the noise figure as a function of the source reflection coefficient and the power measurement results.

[0012] The extraction unit is used to extract the noise parameters of the device under test using noise traction technology with variable input impedance based on the relationship of the system noise figure.

[0013] On the other hand, a computer device is provided, the computer device including a memory and a processor, the memory for storing computer programs, and the processor for executing the computer programs stored in the memory to implement the steps of the fast noise traction method described above.

[0014] On the other hand, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when the computer program is executed by a processor, it implements the steps of the fast noise traction method described above.

[0015] On the other hand, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the fast noise traction method described above.

[0016] The technical solution provided by this invention can bring at least the following beneficial effects:

[0017] The power meter is a high-precision instrument with an accuracy far exceeding that of the noise source, and an uncertainty of less than 0.1 dB. Therefore, calibrating the noise-driven system using the power meter eliminates the need for a noise source. This not only improves system accuracy but also simplifies the hardware structure and connection complexity of the noise-driven system, further simplifying and accelerating the calculation process during noise-driven operation. Clearly, this solution improves both testing speed and accuracy in the noise-driven process. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a rapid noise traction method provided in an embodiment of the present invention;

[0020] Figure 2 This is a structural diagram of a noise traction system provided in an embodiment of the present invention;

[0021] Figure 3 This is a circuit diagram of an input noise module provided in an embodiment of the present invention;

[0022] Figure 4 This is a circuit diagram of an output noise module provided in an embodiment of the present invention;

[0023] Figure 5 This is a structural diagram of another noise traction system provided in an embodiment of the present invention;

[0024] Figure 6 This is another output noise module circuit diagram provided in an embodiment of the present invention;

[0025] Figure 7 This is a structural diagram of a rapid noise traction device provided in an embodiment of the present invention;

[0026] Figure 8 This is a hardware architecture diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0028] The fixed-frequency sweep method in noise-driven systems has been gradually phased out due to its slow testing speed, small adjustable reflection coefficient range of electronic impedance tuners, and poor consistency. The fixed-point sweep method uses a vector network analyzer as its core and employs a cold source method for measurement. After calibrating the source reflection position, the mechanical position of each point remains stationary, and the noise figure is measured frequency-by-frequency before moving to the next position. Although this method significantly improves testing speed and accuracy compared to the fixed-frequency sweep method, all noise-driven systems use the noise source as the noise calibration benchmark. The noise source over-noise ratio uncertainty is typically greater than 0.2 dB and is prone to drift over time, resulting in large measurement uncertainties. This error is directly introduced into the measurement results, affecting the testing accuracy.

[0029] Based on this, the inventive concept of the present invention is to use a power meter to calibrate the source power and then use the accuracy transmission to calibrate the receiver. Since the power meter is a high-precision instrument, its accuracy is much higher than that of the noise source, and its uncertainty is usually less than 0.1dB. Using the power meter for calibration can improve the system accuracy. At the same time, since the noise source is no longer needed, the hardware structure of the noise traction system is simplified and the computational complexity is lower.

[0030] The specific implementation of the above concept is described below.

[0031] Please refer to Figure 1 The present invention provides a rapid noise traction method, which includes:

[0032] Step 100: The noise traction system is pre-calibrated using a power meter to perform the noise traction process using the calibrated noise traction system; the noise traction system does not include a noise source;

[0033] Step 102: After completing the S-parameter calibration, the relationship between the system noise figure and the source reflection coefficient is derived using the noise figure as a function of the source reflection coefficient and the power measurement results.

[0034] Step 104: Based on the relationship of the system noise figure, noise parameters of the device under test are extracted using noise traction technology with variable input impedance.

[0035] In this embodiment of the invention, the power meter is a high-precision instrument with a much higher accuracy than the noise source and an uncertainty of less than 0.1 dB. Therefore, calibrating the noise-driven system using the power meter eliminates the need for a noise source. This not only improves the system's accuracy but also simplifies the hardware structure and connection complexity of the noise-driven system, further simplifying and accelerating the calculation process during noise-driven operation. Thus, this solution can improve both the testing speed and accuracy during noise-driven operation.

[0036] The following description Figure 1 The execution method for each step is shown.

[0037] First, for step 100, the noise traction system is calibrated in advance using a power meter so that the noise traction process can be carried out using the calibrated noise traction system.

[0038] Currently, all noise-driven traction systems use the noise source as the noise calibration benchmark. The noise source's over-noise ratio uncertainty is typically greater than 0.2 dB and is prone to drift over time. In this embodiment of the invention, a power meter is used to calibrate the noise-driven traction system. Specifically, the calibration process of the power meter is as follows: the output power of the signal output port of the vector network analyzer in the noise-driven traction system is calibrated using the power meter to transfer the calibration accuracy to the built-in signal source in the vector network analyzer, so that the calibration accuracy is transferred to the receiver after the receiver and the signal source are connected.

[0039] Since the power meter is a high-precision instrument, its accuracy is much higher than that of the noise source, and its uncertainty is usually less than 0.1dB. Using the power meter for calibration can improve the system accuracy and eliminate the need for a noise source. Therefore, in this embodiment of the invention, the noise traction system used in the noise traction process does not include a noise source.

[0040] When considering different upper limits of the receiver's measurement frequency, such as the frequency band below millimeter wave or the frequency band above millimeter wave, different noise pulling systems can be used. These noise pulling systems can be divided into two architectures, which will be described below.

[0041] For the first option, please refer to [the original text]. Figure 2 When the upper limit of the receiver's measurement frequency is higher than the required test frequency, the noise-driven system includes: a vector network analyzer, an input noise module, an impedance tuner, an output noise module, and a DC regulated power supply; please refer to [link to relevant documentation]. Figure 3 and Figure 4 , respectively Figure 2 The circuit diagrams for the input and output noise modules of the noise-driven system are shown. The input noise module eliminates the need for a switch, instead utilizing a DC bias network consisting of capacitors and inductors. The output noise module includes a DC bias network, a single-pole double-throw switch, a pass-through path, and a low-noise amplification path. The RF DC output of the input noise module is connected to the signal output port of the vector network analyzer, and its RF input is connected to one end of an impedance regulator. Its DC input is connected to a DC regulated power supply. The RF input of the output noise module is connected to the noise measurement port of the vector network analyzer, and its DC input is connected to a DC regulated power supply. The RF DC output of the output noise module and the other end of the impedance regulator are connected to the two ends of the device under test (DUT). The signal output port is... Figure 2 Port 1 in the diagram, the noise measurement port is... Figure 2 Port 2 in the middle.

[0042] The second option, please refer to Figure 5 When the upper limit of the receiver's measurement frequency is lower than the required test frequency, the noise-driven system includes: a vector network analyzer, a first spread spectrum module, an impedance tuner, an output noise module, a second spread spectrum module, and a DC regulated power supply; wherein, the input noise module can be integrated into the first spread spectrum module or the probe; please refer to [link to relevant documentation]. Figure 6 Here is the circuit diagram for the output noise module. Figure 6 Compared to Figure 4 A down-conversion device is added to the low-noise amplification path of the output noise module. The RF DC output terminal of the first spread spectrum module is connected to the signal output port of the vector network analyzer through the spread spectrum controller. The RF input terminal of the first spread spectrum module is connected to one end of the impedance coordinator, and the DC input terminal of the first spread spectrum module is connected to the DC regulated power supply. The RF input terminal of the output noise module is connected to the noise measurement port of the vector network analyzer, and the DC input terminal of the output noise module is connected to the DC regulated power supply. The RF DC output terminal of the output noise module and the other end of the impedance coordinator are connected to the two ends of the device under test. The noise test port of the output noise module is connected to the idle port of the vector network analyzer through the second spread spectrum module and the spread spectrum controller. A signal source local oscillator is also input to the low-noise amplification path of the output noise module. The idle port is the remaining port excluding the signal output port and the noise measurement port, typically port 3.

[0043] In one implementation, the upper limit of the measurement frequency can be 50 GHz or 70 GHz.

[0044] In this embodiment of the invention, the optimized noise traction system simplifies the system hardware structure and system connection complexity, making the operation steps and subsequent noise traction methods simpler and improving system stability. Using a power meter for calibration can effectively suppress the error introduced by the noise source over-noise ratio uncertainty in other schemes. Furthermore, power meter matching is generally superior to that of the noise source, and the calibration process does not require switching between hot and cold states, eliminating the need to correct for the impact of hot and cold state matching differences of the noise source on the test results, thus effectively suppressing the impact of power calibration material mismatch error on the test results.

[0045] Then, for step 102, after completing the S-parameter calibration, the relationship of the system noise figure is derived using the noise figure as a function of the source reflection coefficient and the power measurement results.

[0046] After system calibration using a power meter, S-parameter calibration is then performed. In this embodiment of the invention, precision calibration components and self-calibration methods are preferred. In one implementation, coaxial calibration can use data-based SOLT (Short Open Load Through), SOLR (Short Open Load Reciprocal-through), mechanical calibration components, precision electronic calibration components, and MTRL (Multiple-line Through Reflection Line method) calibration components; on-chip calibration can use MTRL and LRRM (Line Reflection-open Reflection-short Match method) calibration methods.

[0047] After completing the S-parameter calibration, noise parameter calibration is required. During the noise parameter calibration process, the relationship between the system noise figure and the source reflection coefficient can be derived using the noise figure as a function of the noise figure and the power measurement results.

[0048] Specifically, this step may include the following:

[0049] A1: Construct the following function of the noise figure with respect to the source reflection coefficient:

[0050]

[0051] in, F DUT ( CS () represents the noise figure of the device under test with respect to the source reflection coefficient. C S The function; F Sys ( C S () represents the system noise figure with respect to the source reflection coefficient. C S The function; F Rcvr ( C S () represents the receiver noise figure with respect to the source reflection coefficient. C S The function; G A_DUT The asset gain of the device under test;

[0052] A2: The derived formula for the receiver noise figure is:

[0053]

[0054] in, P Cold_Cal The power measurement value at the receiver power detection end face when the noise source is in a cold state during the calibration process; k is the Boltzmann constant, which is 1.380649 × 10⁻⁶. -23 J / K; G A_Rcvr The overall available gain from the receiver system measurement end face to the power detection end face; C L The load reflection coefficient; T 0 represents the ambient temperature; B This represents the bandwidth of the receiver's intermediate frequency filter.

[0055] A3: The relationship between the system noise figure and the receiver noise figure is derived using the relationship between the receiver noise figure and the receiver noise figure.

[0056] In one embodiment of the present invention, after obtaining the relationship of the receiver noise figure, the relationship of the system noise figure can be directly derived.

[0057] In another embodiment of the present invention, considering that the through-pass is generally a non-insertable through-pass, the source reflection coefficient after correction for insertion loss and return loss introduced by the through-pass standard can be further determined as follows:

[0058]

[0059] in, C ST This is the corrected source reflection coefficient. S ijT For each S-parameter of the through standard part;

[0060] During testing of the fast noise traction system, the noise source was kept cold. Mismatches at various points were considered, and the physical temperature of the noise receiver was adjusted. T Rcvr The effect will affect the corrected source reflection coefficient. C ST As the source reflection coefficient C S Substituting these values ​​into the receiver noise figure equation, the system noise figure equation is derived as follows:

[0061]

[0062] in, F Sys ( C S () represents the system noise figure with respect to the source reflection coefficient. C S The function; P Cold_DUT The power measurement value from the receiver system measurement end face to the power detection end face; S ij _ DUT These are the S-parameters of the test piece; T Rcvr This refers to the receiver's physical temperature.

[0063] In this embodiment of the invention, the noise-driven system uses a power meter to calibrate the source power and transmits the calibration to the receiver. The power meter, acting as a higher-level precision instrument, has a much higher accuracy than the noise source. This calibration method improves system accuracy. Furthermore, since the noise source is no longer needed, the input terminal no longer requires a switch for path switching, further simplifying the system hardware configuration, improving accuracy, and reducing costs. During calibration of the noise-driven system in this embodiment of the invention, the available gain from the noise receiver system's measurement end face to the power detection end face is... G A_Rcvr It can be calculated using the following formula:

[0064]

[0065] in, P Rcvr_Cal The power value measured by the receiver after calibration. P Src_Cal The power value of the signal source after calibration. C Pcal To calibrate the reflection coefficient of the signal source power calibration end face, S ij _ Before The S-parameters of the test piece before calibration.

[0066] The current cold / hot source calibration method uses the following formula to calculate the available gain from the measurement end face to the power detection end face of the noise receiver system:

[0067]

[0068] in, P Hot_Cal The power measurement value from the receiver system's measurement end face to the power detection end face when the noise source is in a hot state; C Hot_NS The thermal reflection coefficient of the noise source. C Cold_NS The cold reflection coefficient of the noise source; T Cold This is the cold state temperature.

[0069] As can be seen, in the embodiments of the present invention, the available gain from the measurement end face to the power detection end face of the noise receiver system is calculated. G A_Rcvr Compared to the current cold and heat source calibration method, this method offers improvements in both accuracy and computational simplicity.

[0070] Finally, regarding step 104, based on the relationship of the system noise figure, the noise parameters of the device under test are extracted using noise traction technology with variable input impedance.

[0071] In this embodiment of the invention, the noise traction system uses a power meter as the noise correction benchmark. The core principle of noise correction is that noise testing is essentially testing the noise power spectral density, and the power accuracy is transmitted to each receiver by the power meter. Therefore, the input power, output power, cascaded noise figure, and noise figure of each stage can be calculated using the following first, second, third, and fourth formulas.

[0072] The first formula, the second formula, the third formula, and the fourth formula are as follows:

[0073] ,in,

[0074]

[0075]

[0076]

[0077] In the first formula, the input power P In From signal S In and noise N In Composition; in the second formula, P OutFor output power, G A_DUT For the gain of the tested component, N DUT The third formula is the additional noise power generated by the device under test; it is the cascade noise formula, which can be used to solve for the noise figure of each part after system calibration is completed. F Total The cascade noise figure, F 1. F 2. F 3, ... F n These represent the noise figures for each level, and n is the total number of sampling points; These represent the resource gains at each level; in the fourth formula, the noise figure... F Defined by each noise parameter, F Min To achieve the minimum noise figure, R N Noise electronic resistor, C Opt The optimal impedance point C S This represents the current input impedance.

[0078] As shown in the fourth formula, the noise figure is affected by the input impedance. Therefore, this embodiment of the invention uses variable input impedance and noise-pulling technology to accurately measure the noise parameters of the device under test. The core principle is to reconstruct the fourth formula into the following fifth formula:

[0079]

[0080] Where A, B, C, and D are all variables to be determined, and the relationship between the variables to be determined and each parameter is as follows:

[0081]

[0082] G Opt and B Opt for G Opt The real and imaginary parts of admittance.

[0083] Thus, the four unknowns that originally needed to be solved in the fourth formula can now be solved by only the unknowns in the fifth formula. A , B , C and D The solution can be obtained by performing the calculation.

[0084] Then, the following error function is constructed:

[0085]

[0086] in, e Used to characterize error values, i for n The first sampling point in the nth sampling point i One sampling point, F i For the first i The noise figure calculated from each sampling point; G i and B i The admittance value corresponding to the i-th sampling point G i The real and imaginary parts.

[0087] Next, for the error function, the solution is obtained by finding the extrema through partial differential equations. e Using the following formulas (6, 7, 8, and 9) respectively... A , B , C , D Find the points where the partial derivatives are zero, where the coefficients are obtained using the tenth formula. P Define it.

[0088] Formulas 6, 7, 8, 9, and 10 are as follows:

[0089]

[0090]

[0091]

[0092]

[0093]

[0094] When taking the extreme value, the sixth, seventh, eighth, and ninth formulas mentioned above must be satisfied simultaneously. At this time, the tenth formula equals zero. There are four variables, and theoretically, the system of equations constructed using four sampling points can be solved. In one embodiment of the present invention, to improve data accuracy, the noise traction system can select 22 points on the Smith chart to solve the overdetermined equations shown below:

[0095]

[0096] The matrix parameters in the left-hand side matrix of the above overdetermined equations are as follows:

[0097]

[0098] It should be noted that, for the 22 sampling points, in actual calculations, the selection of sampling points will be based on the quality of the data, using preset conditions to process the ill-conditioned equations. The following principles apply to the processing of the ill-conditioned equations: high-precision arithmetic operations are used; preprocessing methods, such as reference gain, are employed. S 21 Select a positive gain point; employ special numerical solutions or find the cause of ill-conditioned problems to modify the original problem. Common methods include performing Singular Value Decomposition (SVD) on the matrix to remove local singular values ​​or adding a regularization term.

[0099] In solving overdetermined equations, the overdetermined equations can be transformed into positive definite equations using the least second-order multiplication method. The transformed positive definite equations are as follows:

[0100]

[0101] M, X, and Y are all transformation variables, and the superscript T represents the transpose matrix.

[0102] Based on the positive definite equations, the overdetermined equations can be processed for noise dragging according to the following eleventh and twelfth formulas:

[0103]

[0104]

[0105] in, a i , b i , c i , d i Let be the coefficients of the observation equation (i.e., the tenth formula) for the i-th measurement at the i-th sampling point. e i , f i , g i , h i The coefficients of the positive definite fourth-order matrix. y 1. y 2. y 3. y 4 represents the intermediate calculation parameter on the right-hand side of the equation after positive definiteness. In this embodiment of the invention, to improve the stability of the algorithm and reduce its sensitivity to ill-conditioned values, the data can be weighted and solved iteratively.

[0106] After transforming the overdetermined equations into positive definite equations, the Choreski decomposition method can be used as shown in the following formulas: 13th, 14th, 15th, 16th, 17th, 18th, 19th, and 20th (if the matrix [ N ]for n If an dimensional symmetric positive definite matrix exists, then there exists a lower triangular real matrix whose diagonal elements are positive. L ], such that: when [...] L When the diagonal elements of [ ] are positive, this decomposition is unique and is called the Jolesi decomposition. Numerical solutions are then performed:

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114]

[0115] in, n ij , l ij Representing matrices respectively N ]、[ L The elements in ] . x The answer is [A, B, C, D]. T .

[0116] The unexplained parameters in the above formulas thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, and twentieth are intermediate parameters of the Joleski decomposition.

[0117] Thus, ABCD can be obtained. Substituting ABCD into the relationship between the variable to be solved and each parameter, the four unknowns in the relationship can be calculated. These four unknowns are the noise parameters.

[0118] However, in the above-mentioned transformation to ordinary least squares, the weights of each equation are the same. Some data may deviate significantly from the overall objective function, leading to large biases in the estimated values ​​during computation—this is known as ill-conditioned equations, requiring optimization algorithms to suppress them. In this embodiment of the invention, the noise parameter supports methods such as Iterative Reweighted Least Squares (IRLS), Conjugate Gradient (CG), and Singular Value Decomposition (SVD) to solve ill-conditioned overdetermined equations. Compared to IRLS and CG, SVD yields data with better stability. Based on this, the optimization algorithm in this embodiment of the invention uses multiple sets of selected points and large datasets to solve for the statistical median, achieving very satisfactory results.

[0119] The noise-pulling method of this invention simplifies the measurement process and optimizes the test logic. The impedance regulator position is now set to a "fixed-point frequency sweep" mode instead of the traditional "fixed-frequency point sweep" mode that covers the entire circle. As long as sufficient preset impedance points are distributed within the measured frequency band, aliasing can be avoided. In the fixed-point frequency sweep mode, a single frequency scan can be performed at one impedance regulator position, simultaneously measuring S-parameters and noise figure. Then, the impedance regulator position is changed, and data is reacquired. Because the impedance regulator moves relatively few times (e.g., 22 times), the measurement speed is extremely fast, requiring only a few seconds per point—hundreds of times faster than the "fixed-frequency point sweep" mode.

[0120] Please refer to Figure 7 This invention provides a rapid noise traction device, which includes:

[0121] The calibration unit 700 is used to pre-calibrate the noise traction system using a power meter, so as to perform the noise traction process using the calibrated noise traction system; the noise traction system does not include a noise source;

[0122] The processing unit 702 is used to derive the relationship between the system noise figure and the source reflection coefficient by using the noise figure as a function of the source reflection coefficient and the power measurement results after the S-parameter calibration is completed.

[0123] Extraction unit 704 is used to extract the noise parameters of the device under test using noise traction technology with variable input impedance based on the relationship of the system noise figure.

[0124] In one embodiment of the present invention, the calibration unit is specifically used to calibrate the output power of the signal output port of the vector network analyzer in the noise traction system using a power meter, so as to transmit the calibration accuracy to the built-in signal source in the vector network analyzer, so that the calibration accuracy is transmitted to the receiver after the receiver and the signal source are connected, thereby realizing the calibration of the receiver.

[0125] In one embodiment of the present invention, when the upper limit of the measurement frequency of the receiver is higher than the required test frequency, the noise-driven system includes: a vector network analyzer, an input noise module, an impedance tuner, an output noise module, and a DC regulated power supply.

[0126] When the upper limit of the receiver's measurement frequency is lower than the required test frequency, the noise-driven system includes: a vector network analyzer, a first spread spectrum module, an impedance tuner, an output noise module, a second spread spectrum module, and a DC regulated power supply.

[0127] In one embodiment of the present invention, when the processing unit executes the derivation of the system noise figure relationship using the noise figure as a function of the source reflection coefficient and the power measurement results, it specifically includes:

[0128] Construct the following function of the noise figure with respect to the source reflection coefficient:

[0129]

[0130] in, F DUT ( C S () represents the noise figure of the device under test with respect to the source reflection coefficient. C S The function; F Sys ( C S () represents the system noise figure with respect to the source reflection coefficient. C S The function; F Rcvr ( C S () represents the receiver noise figure with respect to the source reflection coefficient. C S The function; G A_DUT The asset gain of the device under test;

[0131] The derived formula for the receiver noise figure is as follows:

[0132]

[0133] in, P Cold_Cal The power measurement value at the receiver power detection end face when the noise source is in a cold state during the calibration process; k is the Boltzmann constant; G A_Rcvr The available gain from the receiver system measurement end face to the power detection end face; C L The load reflection coefficient;T 0 represents the ambient temperature; B This refers to the bandwidth of the receiver's intermediate frequency filter.

[0134] The relationship between the receiver noise figure and the system noise figure is derived using the relationship between the receiver noise figure and the system noise figure.

[0135] In one embodiment of the present invention, the processing unit is further configured to:

[0136] The source reflection coefficient, after correction for insertion loss and return loss introduced by the through-type standard component, is determined as follows:

[0137]

[0138] in, C ST This is the corrected source reflection coefficient. S ijT For each S-parameter of the through standard part;

[0139] The corrected source reflection coefficient C ST As the source reflection coefficient C S Substituting these values ​​into the receiver noise figure equation, the system noise figure equation is derived as follows:

[0140]

[0141] in, F Sys ( C S () represents the system noise figure with respect to the source reflection coefficient. C S The function; P Cold_DUT The power measurement value from the receiver system measurement end face to the power detection end face; S ij _ DUT These are the S-parameters of the test piece; T Rcvr This refers to the receiver's physical temperature.

[0142] In one embodiment of the present invention, the available gain from the receiver system measurement end face to the power detection end face is... G A_Rcvr It is calculated using the following formula:

[0143]

[0144] in, P Rcvr_Cal The power value measured by the receiver after calibration. P Src_CalThe power value of the signal source after calibration. C Pcal To calibrate the reflection coefficient of the signal source power calibration end face, S ij _ Before The S-parameters of the test piece before calibration.

[0145] It should be noted that the rapid noise traction device provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the rapid noise traction device and the rapid noise traction method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0146] Embodiments of this application also provide a computer device, please refer to... Figure 8 The computer device includes a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, at least one program, code set or instruction set being loaded and executed by the processor to implement the fast noise traction method provided in the above-described method embodiments.

[0147] Embodiments of this application also provide a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the fast noise traction method provided in the above-described method embodiments.

[0148] Embodiments of this application also provide a computer program product comprising a computer program, wherein a processor of a computer device reads the computer program from a computer-readable storage medium, and the processor executes the computer program, causing the computer device to perform any of the fast noise traction methods described in the above embodiments.

[0149] For ease of description, the above systems or devices are described separately as various modules or units based on their functions. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware components.

[0150] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0151] Finally, it should be noted that in this document, relational terms such as first, second, third, and fourth are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0152] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A rapid noise traction method, characterized in that, The method includes: The noise traction system is pre-calibrated using a power meter to perform the noise traction process using the calibrated noise traction system; the noise traction system does not include a noise source. After completing the S-parameter calibration, the relationship between the system noise figure and the source reflection coefficient is derived using the noise figure as a function of the source reflection coefficient and the power measurement results. Based on the relationship of the system noise figure, the noise parameters of the device under test are extracted using noise traction technology with variable input impedance. The relationship between the system noise figure and the source reflection coefficient is derived using the noise figure as a function of the source reflection coefficient and power measurement results, including: Construct the following function of the noise figure with respect to the source reflection coefficient: in, F DUT ( Γ S () represents the noise figure of the device under test with respect to the source reflection coefficient. Γ S The function; F Sys ( Γ S The system noise figure is given by the source reflection coefficient. Γ S The function; F Rcvr ( Γ S () represents the receiver noise figure with respect to the source reflection coefficient. Γ S The function; G A_DUT The asset gain of the device under test; The derived formula for the receiver noise figure is as follows: in, P Cold_Cal The power measurement value at the receiver power detection end face when the noise source is in a cold state during the calibration process; k is the Boltzmann constant; G A_Rcvr The available gain from the receiver system measurement end face to the power detection end face; Γ L The load reflection coefficient; T 0 represents the ambient temperature; B This refers to the bandwidth of the receiver's intermediate frequency filter. The relationship between the receiver noise figure and the system noise figure is derived using the relationship between the receiver noise figure and the system noise figure.

2. The method according to claim 1, characterized in that, The calibration of the noise traction system using a power meter includes: The output power of the signal output port of the vector network analyzer in the noise traction system is calibrated using a power meter, so as to transfer the calibration accuracy to the built-in signal source in the vector network analyzer, so that the calibration accuracy is transferred to the receiver after the receiver and the signal source are connected.

3. The method according to claim 1, characterized in that, When the upper limit of the receiver's measurement frequency is higher than the required test frequency, the noise-driven system includes: a vector network analyzer, an input noise module, an impedance tuner, an output noise module, and a DC regulated power supply. When the upper limit of the receiver's measurement frequency is lower than the required test frequency, the noise-driven system includes: a vector network analyzer, a first spread spectrum module, an impedance tuner, an output noise module, a second spread spectrum module, and a DC regulated power supply.

4. The method according to claim 1, characterized in that, Also includes: The source reflection coefficient, after correction for insertion loss and return loss introduced by the through-type standard component, is determined as follows: in, Γ ST This is the corrected source reflection coefficient. S ijT For each S-parameter of the through standard part; The corrected source reflection coefficient Γ ST As the source reflection coefficient Γ S Substituting these values ​​into the receiver noise figure equation, the system noise figure equation is derived as follows: in, F Sys ( Γ S The system noise figure is given by the source reflection coefficient. Γ S The function; P Cold_DUT The power measurement value from the receiver system measurement end face to the power detection end face; S ij _ DUT These are the S-parameters of the test piece; T Rcvr This refers to the receiver's physical temperature.

5. The method according to claim 4, characterized in that, Available gain from the receiver system measurement face to the power detection face G A_Rcvr It is calculated using the following formula: in, P Rcvr_Cal The power value measured by the receiver after calibration. P Src_Cal The power value of the calibrated signal source. Γ Pcal To calibrate the reflection coefficient of the signal source power calibration end face, S ij _ Before The S-parameters of the test piece before calibration.

6. A rapid noise traction device, characterized in that, The apparatus for performing the rapid noise traction method according to any one of claims 1-5, the apparatus comprising: A calibration unit is used to pre-calibrate the noise traction system using a power meter, so as to perform the noise traction process using the calibrated noise traction system; the noise traction system does not include a noise source; The processing unit is used to derive the relationship between the system noise figure and the source reflection coefficient after the S-parameter calibration is completed, using the noise figure as a function of the source reflection coefficient and the power measurement results. The extraction unit is used to extract the noise parameters of the device under test using noise traction technology with variable input impedance based on the relationship of the system noise figure.

7. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the steps of the method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method described in any one of claims 1-5.

9. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1-5.

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