Noise de-embedding method and device

Abstract

This invention discloses a noise de-embedding method and apparatus, belonging to the field of chip measurement. The method includes: acquiring the S-parameter measurements and noise parameter measurements of the device under test (DUT), and acquiring the S-parameter measurements of a preset de-embedding structure; all acquired measurements are obtained after S-parameter calibration and noise calibration; calculating the equivalent capacitance and equivalent inductance values ​​in the load model based on the S-parameter measurements and the known resistance value in the resistor-inductor-capacitor load model; the circuit connection of the load model is as follows: the capacitor and resistor form a parallel circuit, which is connected in series with the inductor; calculating the true reflection coefficient of the load based on the load model, and substituting the true reflection coefficient into the de-embedding algorithm to calculate the S-parameters of the structure to be de-embedding; and calculating the true noise parameters of the DUT after de-embedding based on the S-parameters of the structure to be de-embedding, the S-parameter measurements of the DUT, and the noise parameter measurements of the DUT. The noise de-embedding results obtained by this invention have higher accuracy.

Description

Technical Field

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

[0002] When using RF probes to measure the characteristic parameters of a Device Under Test (DUT), test pads and a passive metal interconnect structure need to be introduced at both ends of the DUT. Therefore, the measurement results include not only the characteristic parameters of the DUT but also the parasitic parameters of the metal leads and pads. To obtain accurate characteristic parameters of the DUT, de-embedding techniques are needed to de-embed the metal leads and pads (referred to as the de-embedding structure) to extract the true characteristic parameters.

[0003] In related technologies, de-embedding methods are used to simultaneously de-embedding noise and S-parameters of the device under test. These methods include open-circuit / short-circuit de-embedding, one-step calibration de-embedding, and electromagnetic simulation de-embedding. However, the de-embedding accuracy of these technologies is relatively poor.

[0004] Therefore, there is an urgent need to provide a noise de-embedding method with higher de-embedding accuracy. Summary of the Invention

[0005] This invention provides a noise de-embedding method and apparatus. The technical solution is as follows:

[0006] On the one hand, a noise de-embedding method is provided, the method comprising:

[0007] The S-parameter and noise parameter measurements of the device under test are obtained, as well as the S-parameter measurement of the preset de-embedding structure; all the measurements are obtained after S-parameter calibration and noise calibration.

[0008] Based on the measured S-parameter values ​​and the known resistance value in the resistance-inductance-capacitor load model, calculate the equivalent capacitance and equivalent inductance values ​​in the load model; the circuit connection of the load model is as follows: the capacitor and the resistor form a parallel circuit, and this parallel circuit is connected in series with the inductor.

[0009] The load true reflection coefficient is calculated based on the load model, and the load true reflection coefficient is substituted into the de-embedding algorithm to calculate the S-parameters of the structure to be de-embedding.

[0010] Based on the S-parameters of the structure to be de-embedded and the measured S-parameters and noise parameters of the device under test, the true noise parameters of the device under test after de-embedding are calculated.

[0011] On the other hand, a noise de-embedding device is provided, the device comprising:

[0012] The acquisition unit is used to acquire the S-parameter and noise parameter measurements of the device under test, and to acquire the S-parameter measurements of a preset de-embedding structure; all acquired measurements are obtained after S-parameter calibration and noise calibration.

[0013] The first calculation unit is used to calculate the equivalent capacitance and equivalent inductance values ​​in the load model based on the measured S-parameter values ​​and the known resistance values ​​in the resistance-inductance-capacitor load model. The circuit connection of the load model is as follows: the capacitor and the resistor form a parallel circuit, which is connected in series with the inductor. The true reflection coefficient of the load is calculated based on the load model, and the true reflection coefficient of the load is substituted into the de-embedding algorithm to calculate the S-parameters of the structure to be de-embedded.

[0014] The second calculation unit is used to calculate the true noise parameters of the device under test after de-embedding based on the S-parameters of the structure to be de-embedded and the measured values ​​of the S-parameters and noise parameters of the device under test.

[0015] On the other hand, a computer device is provided, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for executing the computer program stored in the memory to implement the steps of the noise de-embedding method described above.

[0016] 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 noise de-embedding method described above.

[0017] 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 noise de-embedding method described above.

[0018] The technical solution provided by this invention can bring at least the following beneficial effects: by modifying the load model from the traditional inductor-resistor load model to a resistor-inductor-capacitor load model, the load model is more in line with the actual physical structure of the load, and the S-parameters of the structure to be de-embedded are calculated with higher accuracy through the improved load model. As a result, when the S-parameters of the structure to be de-embedded are used to calculate the real noise parameters of the device under test after de-embedding, the calculated noise de-embedding result is more accurate. Attached Figure Description

[0019] 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.

[0020] Figure 1 This is a flowchart of a noise removal method provided in an embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of a load model circuit structure provided in an embodiment of the present invention;

[0022] Figure 3 This is a structural diagram of a noise de-embedding device provided in an embodiment of the present invention;

[0023] Figure 4 This is a hardware architecture diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0024] 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.

[0025] Please refer to Figure 1 The present invention provides a noise de-embedding method, the method comprising:

[0026] Step 100: Obtain the S-parameter measurement value and noise parameter measurement value of the device under test, and obtain the S-parameter measurement value of the preset de-embedding structure; all the obtained measurement values ​​are obtained after S-parameter calibration and noise calibration.

[0027] Step 102: Based on the measured S-parameter values ​​and the known resistance values ​​in the resistance-inductance-capacitor load model, calculate the equivalent capacitance and equivalent inductance values ​​in the load model; the circuit connection of the load model is as follows: the capacitor and the resistor form a parallel circuit, and this parallel circuit is connected in series with the inductor.

[0028] Step 104: Calculate the true load reflection coefficient based on the load model, and substitute the true load reflection coefficient into the de-embedding algorithm to calculate the S-parameters of the structure to be de-embedded.

[0029] Step 106: Calculate the true noise parameters of the device under test after de-embedding based on the S-parameters of the structure to be de-embedded and the measured S-parameters and noise parameters of the device under test.

[0030] In this embodiment of the invention, the load model is modified from the traditional inductor-resistor load model to a resistor-inductor-capacitor load model, making the load model more consistent with the actual physical structure of the load. The S-parameters of the structure to be de-embedded are calculated with higher accuracy through the improved load model. As a result, when the S-parameters of the structure to be de-embedded are used to calculate the real noise parameters of the device under test after de-embedding, the calculation results are more accurate.

[0031] The following description Figure 1 The execution method for each step is shown.

[0032] First, for step 100, the S-parameter measurement value and noise parameter measurement value of the device under test are obtained, and the S-parameter measurement value of the preset de-embedding structure is also obtained; all the obtained measurement values ​​are obtained after S-parameter calibration and noise calibration.

[0033] In this embodiment of the invention, a de-embedding structure is provided on the device under test (wafer). The de-embedding structure can be preset to different states: Thru, Reflect-Open, Reflect-Short, and Match. The de-embedding structure is identical to the pads and metal wires (the structure to be de-embedding) between the end face of the device under test and the probes. Thus, the S-parameters of the structure to be de-embedding can be determined using the measured S-parameter values ​​of the preset de-embedding structure.

[0034] When measuring the parameters of the device under test (DUT) and the preset de-embedding structure, a vector network analyzer is first required. The initial settings of the vector network analyzer can be pre-configured, including at least one of the following: start and end frequencies, frequency step, output power, intermediate frequency bandwidth, averaging cycles, and attenuator settings. After warm-up, connect the probes and cables, and level the probes. Then, select a suitable calibration method to calibrate the S-parameters and noise. After calibration, measure the S-parameters and noise parameters. Preferably, the calibration method can use the MTRL (Multiple-line Through Reflection Line method) or LRRM (Line Reflection-open Reflection-short Match method) S-parameter calibration method. Noise calibration can be achieved based on the noise pulling method of the vector network analyzer.

[0035] After measurement, the S-parameter and noise parameter values ​​of the device under test (DUT) can be obtained, as well as the S-parameter values ​​for shoot-through, open-circuit reflection, short-circuit reflection, and load matching obtained by measuring the preset de-embedding structure. It can be understood that the measured values ​​obtained for the DUT at this point include the values ​​of the de-embedding structure to be determined.

[0036] It should be noted that each measurement includes multiple values, and these multiple values ​​correspond one-to-one with multiple frequency points. That is, for multiple frequency points, each frequency point corresponds to one S-parameter measurement value of the device under test, one noise parameter measurement value of the device under test, and one S-parameter measurement value of a preset de-embedding structure.

[0037] In this embodiment of the invention, after obtaining the measurement value, it is necessary to de-embed the measurement value. It can be seen that the calibration process and the de-embedding process in this embodiment of the invention are independent and separate. The calibration process is implemented during the measurement process, while the de-embedding process can be implemented offline after the measurement is completed, which makes it more flexible in practical applications.

[0038] Then, steps 102 and 104 will be explained simultaneously.

[0039] This invention employs an improved LRRM de-embedding method to extract the S-parameters of the structure to be de-embedded. This improved LRRM de-embedding method differs from the traditional LRRM algorithm in its load handling. The traditional LRRM algorithm equates the load of the de-embedded structure to an inductor-resistor load model. However, in this invention, to make the load model more physically accurate and improve de-embedding accuracy, the load of the de-embedded structure is equated to a resistor-inductor-capacitor load model. The circuit connection of this load model is as follows: the capacitor and resistor form a parallel circuit, which is then connected in series with the inductor. Please refer to [reference needed]. Figure 2 This is a schematic diagram of the load model structure. The resistance value in the load model is known, or can be accurately measured using the four-wire method, and is typically 50Ω.

[0040] In this embodiment of the invention, the equivalent capacitance and equivalent inductance values ​​in the load model can be calculated in the following manner:

[0041] First, the equivalent capacitance value in the load model is calculated: based on the S-parameter measurements of the preset de-embedding structure, the reflection coefficient of the open-circuit de-embedding structure corresponding to each frequency point is determined; according to each frequency point and its corresponding open-circuit de-embedding structure reflection coefficient, the capacitance value corresponding to each frequency point is calculated; the equivalent capacitance value is obtained by calculating the capacitance value of each frequency point using a weighted average algorithm. Here, there are multiple S-parameter measurements, and each of these multiple S-parameter measurements corresponds one-to-one with multiple frequency points.

[0042] The reflection coefficient of the open-circuit de-embedding structure is obtained from the S-parameter measurements when the de-embedding structure is in the state of open reflection.

[0043] The capacitance value C at frequency point f is calculated using the following formula:

[0044]

[0045] Where Z0 is the characteristic impedance of the probe, Γ O1,stdThe reflection coefficient of the open-circuit de-embedded structure.

[0046] After obtaining the capacitance value at each frequency point, the equivalent capacitance value C is calculated using the following weighted average algorithm. e :

[0047]

[0048] Where n is the number of frequency points, the vector network analyzer uses n operating frequencies for measurement.

[0049] After obtaining the equivalent capacitance value in the load model, the equivalent inductance value in the load model is calculated using the following method:

[0050] The inductance L at frequency f in the load model is solved using the following formula to obtain two first inductance values:

[0051]

[0052] Among them, R M Given the resistance value, C e Where B is the equivalent capacitance, and G is the susceptance. O and B O These are the conductance and susceptance of the open-circuit de-embedding structure, respectively.

[0053] The second inductance value at frequency point f is obtained by solving the resistor-inductor load model using the LRRM de-embedding algorithm;

[0054] The first inductance value, which has the smallest error with the second inductance value, is determined as the true inductance value obtained from the solution.

[0055] The formula used to calculate the first inductance value is obtained through the proportional relationship of the LRRM de-embedding algorithm. Rearranging this formula, we can obtain the following quadratic equation:

[0056] Ux 2 +Vx+W=0

[0057] The coefficients U, V, and W are represented by the following formulas:

[0058] U = u

[0059] V = -(2uv + 1)

[0060]

[0061] u=2πfC e

[0062] x=2πfL

[0063]

[0064] Where u and v are intermediate coefficients.

[0065] Solving for the inductance value is equivalent to solving the quadratic equation above. The value of x is calculated using the following formula:

[0066]

[0067] Therefore, two roots can be obtained, namely two first inductance values. One of the first inductance values ​​is a true root and the other is a false root. It is necessary to determine which first inductance value is the true root.

[0068] In this embodiment of the invention, when solving the resistor-inductor load model using the traditional LRRM de-embedding algorithm, a second inductance value can be obtained. Since the second inductance value is accurate, the first inductance value with the smallest error can be determined as the true inductance value by comparing it with the second inductance value.

[0069] After obtaining the inductance value at each frequency, the equivalent inductance value L can be calculated using the following weighted average algorithm. e :

[0070]

[0071] Thus, the resistance, inductance, and capacitance in the load model are all known values.

[0072] By substituting the known resistance, equivalent inductance, and equivalent capacitance values ​​into the load model, the true reflection coefficient of the load can be calculated. Then, the S-parameters of the structure to be de-embedded can be calculated using the LRRM de-embedding algorithm. It should be noted that the methods for calculating the true reflection coefficient of the load and the S-parameters of the structure to be de-embedded are existing technologies and will not be elaborated upon here.

[0073] Finally, for step 106, the actual noise parameters of the device under test after de-embedding are calculated based on the S-parameters of the structure to be de-embedded and the measured S-parameters and noise parameters of the device under test.

[0074] After obtaining the S-parameters of the structure to be de-embedded, the true noise parameters of the device under test after de-embedding can be calculated using the following method:

[0075] Based on the S-parameters of the structure to be de-embedded and the measured S-parameters of the device under test, the S-parameters of the device under test after de-embedding are calculated, and the S-parameters of the device under test after de-embedding are converted into the ABCD parameters of the device under test after de-embedding.

[0076] The S-parameters of the structure to be de-embedded are converted into the Y-parameters and ABCD-parameters of the structure to be de-embedded;

[0077] Based on the Y parameters of the structure to be de-embedded, calculate the noise correlation matrix in the form of the transmission parameters of the structure to be de-embedded;

[0078] The true noise parameters of the device under test after de-embedding are calculated using the ABCD parameters of the device to be de-embedded, the ABCD parameters of the structure to be de-embedded, the noise correlation matrix in the form of the transmission parameters of the structure to be de-embedded, and the noise parameter measurement values ​​of the device under test.

[0079] Since the measured S-parameter values ​​of the device under test contain information about the structure to be de-embedded, the S-parameters of the device under test after de-embedding can be calculated using de-embedding techniques after obtaining the S-parameters of the structure to be de-embedded.

[0080] It is understood that noise parameters are related to S-parameters, and noise parameters can be calculated using matrices related to S-parameters. Since Y-parameters cannot be directly cascaded, directly calculating noise parameters using Y-parameters would be very complex. In this embodiment of the invention, noise parameters are calculated using ABCD parameters. Therefore, it is necessary to transform the S-parameters of the de-embedded device under test (DUT) into the ABCD parameters of the de-embedded DUT. Similarly, the S-parameters of the structure to be de-embedded are transformed into the ABCD parameters of the structure to be de-embedded.

[0081] In this embodiment of the invention, after obtaining the S-parameters of the structure to be de-embedded, the S-parameters of the structure to be de-embedded are transformed into Y-parameters, and then the noise correlation matrix in the admittance form of the structure to be de-embedded can be calculated using the Y-parameters [C]. Y ]:

[0082]

[0083] in, These are the noise correlation matrices in admittance form for the left and right pads to be de-embedded in the structure to be de-embedded, respectively. These are the Y parameters for the left and right pads to be de-embedded, respectively; k is the Boltzmann constant 1.380649 × 10⁻⁶. -23 J / K, T is the equivalent noise temperature, and the function Re() is used to find the real part.

[0084] Next, the noise correlation matrix of the structure to be de-embedded is transformed from admittance form to transfer parameter form, resulting in the noise correlation matrix of the structure to be de-embedded in transfer parameter form [C]. A ]:

[0085]

[0086] Among them, [T Y→A [ ] is the transformation matrix for converting the Y parameters to the ABCD parameters. This indicates transpose and conjugate.

[0087] It can be understood that the S-parameters are scattering parameters, the Y-parameters are admittance parameters, and the ABCD parameters are transmission parameters. Furthermore, the conversion methods between the above S-parameters, Y-parameters, and ABCD parameters can all be achieved through the conversion relationships between parameters, which will not be elaborated in this embodiment.

[0088] Finally, the true noise parameters of the device under test after de-embedding are calculated using the following formula:

[0089]

[0090] Among them, [C A ] DUT This represents the true noise parameters of the device under test after de-embedding. For the ABCD parameters of the left pad to be de-embedded, [C A ] Meas The measured value of the noise parameter of the device under test. The noise correlation matrices are in the form of transmission parameters for the left pad to be de-embedded and the right pad to be de-embedded, respectively. [A] DUT [ ] represents the ABCD parameters of the device under test after de-embedding.

[0091] Thus, noise removal was completed.

[0092] This invention employs a noise de-embedding method based on a signal flow graph model. The signal flow graph model is more accurate in describing high-frequency signal characteristics, while methods based on equivalent circuits involve many approximations, resulting in lower high-frequency accuracy. Compared to the equivalent circuit model, this method offers higher accuracy, at least in the DC–110GHz range. Furthermore, the de-embedding and calibration processes are independent and can be performed step-by-step, even offline, eliminating reliance on specific calibration software and offering greater flexibility. The load model is also improved, shifting from the traditional inductor-resistor load model to an inductor-resistor-capacitor load model, which better reflects physical reality and achieves higher accuracy. Moreover, the used open-circuit, short-circuit, load-matching, and through-hole de-embedding structures have a small footprint on the wafer, saving costs, and this approach is a broadband method with a frequency range far exceeding that of the MTRL method family. Finally, this method is based on measured data, offering higher reliability compared to electromagnetic simulation results.

[0093] Please refer to Figure 3 This invention provides a noise de-embedding device, which includes:

[0094] The acquisition unit 301 is used to acquire the S-parameter measurement values ​​and noise parameter measurement values ​​of the device under test, and to acquire the S-parameter measurement values ​​of the preset de-embedding structure; the acquired measurement values ​​are all obtained after S-parameter calibration and noise calibration;

[0095] The first calculation unit 302 is used to calculate the equivalent capacitance and equivalent inductance values ​​in the load model based on the measured S-parameter values ​​and the known resistance values ​​in the resistance-inductance-capacitor load model. The circuit connection of the load model is as follows: the capacitor and the resistor form a parallel circuit, and the parallel circuit is connected in series with the inductor. The true reflection coefficient of the load is calculated based on the load model, and the true reflection coefficient of the load is substituted into the de-embedding algorithm to calculate the S-parameters of the structure to be de-embedded.

[0096] The second calculation unit 303 is used to calculate the true noise parameters of the device under test after de-embedding based on the S-parameters of the structure to be de-embedded and the measured values ​​of the S-parameters and noise parameters of the device under test.

[0097] In one embodiment of the present invention, there are multiple S-parameter measurement values, and the multiple S-parameter measurement values ​​correspond one-to-one with multiple frequency points;

[0098] When calculating the equivalent capacitance and equivalent inductance values ​​in the load model, the first calculation unit specifically includes: determining the reflection coefficient of the open-circuit de-embedding structure corresponding to each frequency point based on the measured S-parameter values ​​of the preset de-embedding structure; calculating the capacitance value corresponding to each frequency point based on the reflection coefficient of the open-circuit de-embedding structure; calculating the equivalent capacitance value by using a weighted average algorithm to calculate the capacitance value of each frequency point; solving for the inductance value corresponding to each frequency point in the load model using the LRRM de-embedding algorithm, and calculating the equivalent inductance value by using a weighted average algorithm to calculate the inductance value of each frequency point.

[0099] In one embodiment of the present invention, when the first computing unit performs the calculation of the inductance value corresponding to each frequency point in the load model according to the LRRM de-embedding algorithm, it specifically includes:

[0100] The inductance L at frequency f in the load model is solved using the following formula to obtain two first inductance values:

[0101]

[0102] Among them, R M Given the resistance value, C e Where B is the equivalent capacitance, and G is the susceptance. O and B O These are the conductance and susceptance of the open-circuit de-embedding structure, respectively.

[0103] The second inductance value at frequency point f is obtained by solving the resistor-inductor load model using the LRRM de-embedding algorithm;

[0104] The first inductance value, which has the smallest error with the second inductance value, is determined as the true inductance value obtained from the solution.

[0105] In one embodiment of the present invention, the second calculation unit is specifically used for: calculating the S-parameters of the device under test after de-embedding based on the S-parameters of the structure to be de-embedded and the measured S-parameter values ​​of the device under test, and converting the S-parameters of the device under test after de-embedding into ABCD parameters of the device under test after de-embedding; converting the S-parameters of the structure to be de-embedded into Y-parameters and ABCD parameters of the structure to be de-embedded; calculating the noise correlation matrix in the form of transmission parameters of the structure to be de-embedded based on the Y-parameters of the structure to be de-embedded; and calculating the true noise parameters of the device under test after de-embedding using the ABCD parameters of the device under test after de-embedding, the ABCD parameters of the structure to be de-embedded, the noise correlation matrix in the form of transmission parameters of the structure to be de-embedded, and the measured noise parameters of the device under test.

[0106] In one embodiment of the present invention, the second calculation unit specifically calculates the true noise parameters of the device under test after de-embedding using the following formula, including:

[0107]

[0108] Among them, [C A ] DUT This represents the true noise parameters of the device under test after de-embedding. For the ABCD parameters of the left pad to be de-embedded, [C A ] Meas The measured value of the noise parameter of the device under test. The noise correlation matrices are in the form of transmission parameters for the left pad to be de-embedded and the right pad to be de-embedded, respectively. [A] DUT [ ] represents the ABCD parameters of the device under test after de-embedding.

[0109] It should be noted that the noise de-embedding 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 noise de-embedding device and the noise de-embedding 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.

[0110] Embodiments of this application also provide a computer device, please refer to... Figure 4 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 noise removal method provided in the above-described method embodiments.

[0111] 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 noise removal method provided in the above-described method embodiments.

[0112] Embodiments of this application also provide a computer program product, which includes a computer program. A processor of a computer device reads the computer program from a computer-readable storage medium and executes the computer program, causing the computer device to perform any of the noise de-embedding methods described in the above embodiments.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A noise de-embedding method, characterized in that, The method includes: The S-parameter and noise parameter measurements of the device under test are obtained, as well as the S-parameter measurement of the preset de-embedding structure. All the measurements are obtained after S-parameter calibration and noise calibration. There are multiple S-parameter measurements, and each of the multiple S-parameter measurements corresponds to a different frequency point. Based on the S-parameter measurements and the known resistance value in the resistor-inductor-capacitor load model, the equivalent capacitance and equivalent inductance values ​​in the resistor-inductor-capacitor load model are calculated, including: determining the reflection coefficient of the open-circuit de-embedding structure corresponding to each frequency point based on the S-parameter measurements of the preset de-embedding structure; calculating the capacitance value corresponding to each frequency point based on the reflection coefficient of the corresponding open-circuit de-embedding structure; calculating the equivalent capacitance value for the capacitance value at each frequency point; and using the following formula to calculate the equivalent capacitance value for the frequency points in the resistor-inductor-capacitor load model. f Inductance at time L The solution process yields two first inductance values. The LRRM de-embedding algorithm is used to solve the resistor-inductor load model, resulting in a second inductance value at frequency f. The first inductance value with the smallest error compared to the second inductance value is determined as the actual inductance value obtained from the solution. The equivalent inductance value is calculated for the actual inductance value at each frequency. The circuit connection of the resistor-inductor-capacitor load model is as follows: the capacitor and resistor form a parallel circuit, which is then connected in series with the inductor. The formula is: in, R M Given the resistance value, C e B is the equivalent capacitance value, and B is the susceptance. G O and B O These are the conductance and susceptance of the open-circuit de-intercalation structure, respectively. The true reflection coefficient of the load is calculated based on the resistor-inductor-capacitor load model, and the true reflection coefficient of the load is substituted into the de-embedding algorithm to calculate the S-parameters of the structure to be de-embedding. Based on the S-parameters of the structure to be de-embedded and the measured S-parameters and noise parameters of the device under test, the true noise parameters of the device under test after de-embedding are calculated.

2. The method as described in claim 1, characterized in that, The equivalent capacitance value is obtained by calculating the capacitance value at each frequency point using a weighted average algorithm. The equivalent inductance value is obtained by calculating the inductance value at each frequency point using a weighted average algorithm.

3. The method as described in claim 1, characterized in that, The calculation of the true noise parameters of the device under test after de-embedding includes: Based on the S-parameters of the structure to be de-embedded and the measured S-parameters of the device under test, the S-parameters of the device under test after de-embedding are calculated, and the S-parameters of the device under test after de-embedding are converted into the ABCD parameters of the device under test after de-embedding. The S-parameters of the structure to be de-embedded are converted into the Y-parameters and ABCD-parameters of the structure to be de-embedded; Based on the Y parameters of the structure to be de-embedded, calculate the noise correlation matrix in the form of the transmission parameters of the structure to be de-embedded; The true noise parameters of the device under test after de-embedding are calculated using the ABCD parameters of the device to be de-embedded, the ABCD parameters of the structure to be de-embedded, the noise correlation matrix in the form of the transmission parameters of the structure to be de-embedded, and the noise parameter measurement values ​​of the device under test.

4. The method as described in claim 3, characterized in that, The process of calculating the true noise parameters of the device under test (DUT) after de-embedding using the ABCD parameters of the DUT, the ABCD parameters of the structure to be de-embedding, the noise correlation matrix in the form of the transmission parameters of the structure to be de-embedding, and the noise parameter measurements of the DUT includes: The true noise parameters of the device under test after de-embedding are calculated using the following formulas, including: in, The true noise parameters of the device under test after de-embedding. The ABCD parameters of the left pad to be de-embedded are: The measured value of the noise parameter of the device under test. , These are the noise correlation matrices in the form of transmission parameters for the left and right pads to be de-embedded, respectively. These are the ABCD parameters of the device under test after de-embedding.

5. A noise de-embedding device, characterized in that, The device includes: The acquisition unit is used to acquire the S-parameter and noise parameter measurements of the device under test, and to acquire the S-parameter measurements of the preset de-embedding structure. The acquired measurements are all obtained after S-parameter calibration and noise calibration. There are multiple S-parameter measurements, and each of the multiple S-parameter measurements corresponds to a different frequency point. The first calculation unit is used to calculate the equivalent capacitance and equivalent inductance values ​​in the resistance-inductance-capacitor load model based on the measured S-parameter values ​​and the known resistance values ​​in the model. This includes: determining the reflection coefficient of the open-circuit de-embedding structure corresponding to each frequency point based on the measured S-parameter values ​​of the preset de-embedding structure; calculating the capacitance value corresponding to each frequency point based on the reflection coefficient of the open-circuit de-embedding structure; calculating the equivalent capacitance value for each frequency point; and using the following formula to calculate the equivalent capacitance value for the resistance-inductance-capacitor load model at frequency points... f Inductance at time L The solution process yields two first inductance values. The LRRM de-embedding algorithm is used to solve the resistor-inductor load model, resulting in a second inductance value at frequency f. The first inductance value with the smallest error compared to the second inductance value is determined as the actual inductance value obtained from the solution. The equivalent inductance value is calculated for the actual inductance value at each frequency. The circuit connection of the resistor-inductor-capacitor load model is as follows: the capacitor and resistor form a parallel circuit, which is connected in series with the inductor. The actual reflection coefficient of the load is calculated based on the resistor-inductor-capacitor load model, and this actual reflection coefficient is substituted into the de-embedding algorithm to calculate the S-parameters of the structure to be de-embedded. The formula is: in, R M Given the resistance value, C e B is the equivalent capacitance value, and B is the susceptance. G O and B O These are the conductance and susceptance of the open-circuit de-intercalation structure, respectively. The second calculation unit is used to calculate the true noise parameters of the device under test after de-embedding based on the S-parameters of the structure to be de-embedded and the measured values ​​of the S-parameters and noise parameters of the device under test.

6. The apparatus as claimed in claim 5, characterized in that, The equivalent capacitance value is obtained by calculating the capacitance value at each frequency point using a weighted average algorithm. The equivalent inductance value is obtained by calculating the inductance value at each frequency point using a weighted average algorithm.

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 1-4.

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 1-4.

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 1-4.

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