Chip test signal analysis processing system and method based on big data

Abstract

The invention discloses a chip test signal analysis processing system and method based on big data, and relates to the technical field of chip test signal analys.The chip test signal analysis processing method comprises the steps that a chip test signal is collected, a target test signal is obtained through signal preprocessing, and a period window is set to obtain a target period signal; performing corresponding period time point signal channel decomposition processing based on the target period signal to obtain channel feature data of corresponding time point signal data; combining the channel feature data to construct a feature association group of the signal data at the corresponding time point, and analyzing the feature state data of the signal at the corresponding time point based on the feature association group corresponding to the signal data at each time point in the period; based on the signal characteristic state data at each time point, carrying out signal state operation steady state analysis at continuous time points in a period, and determining the signal state condition of the chip; according to the method, the real-time operation state characteristics of the chip are effectively analyzed, the operation steady state condition of the chip signal is determined, and accurate measurement and analysis are carried out.

Description

Technical Field

[0001] The present invention relates to the field of chip test signal analysis, and in particular to a chip test signal analysis and processing system and method based on big data. Background Art

[0002] The current semiconductor industry is booming, especially in an environment of rapid technological development, and its hardware and software demands are gradually expanding. Therefore, the current environment has a huge demand for semiconductor chips, which requires high requirements and strict standards for the quality of semiconductor chips;

[0003] Traditional chip signal testing mostly relies on oscilloscopes and other equipment to observe signal waveforms, measure basic parameters such as signal amplitude and period, and compare signal data through small-scale data statistical analysis to establish performance standards; however, it is unable to handle complex signal characteristics. Faced with a large number of digital signals and mixed signals in current chip signals, it is difficult to extract deep-level feature information, and the data scale on which it is based is too small to fully reflect the real-time performance status of the chip. Therefore, traditional chip signal testing methods cannot effectively analyze the real-time operating status characteristics of the chip and determine the steady-state operation of the chip signal, so it is impossible to perform an accurate measurement and analysis. Summary of the invention

[0004] The purpose of the present invention is to provide a chip test signal analysis and processing system and method based on big data to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A chip test signal analysis and processing method based on big data, the method comprising the following steps:

[0007] Collect chip test signals, obtain target test signals through signal preprocessing, and set a periodic window to retrieve target periodic signals from the target test signals; the signal preprocessing includes signal frequency band positioning processing and signal noise reduction processing;

[0008] Based on the target periodic signal, the signal channel of the corresponding periodic time point is decomposed and processed to obtain the channel characteristic data of the signal data at the corresponding time point; the channel characteristic data includes the channel characteristic component and the instantaneous characteristic frequency of the corresponding channel; the characteristic association group of the signal data at the corresponding time point is constructed in combination with the channel characteristic data, and based on the characteristic association group corresponding to the signal data at each time point in the period, the characteristic state data of the signal at the corresponding time point is analyzed, and based on the signal characteristic state data at each time point, the signal state operation steady-state analysis of continuous time points in the period is performed, and the chip signal state is determined based on the analysis data.

[0009] The signal frequency band positioning processing is to use the signal acquisition equipment to perform signal sampling and debugging, adjust the sampling frequency according to the highest frequency of the chip test signal, and obtain the initial signal data of the target test signal in the target frequency range;

[0010] The signal noise reduction processing is to convert the collected initial signal data of the target test signal into the time domain and frequency domain, and remove the interference signal data in the target test signal through a filtering algorithm, and output the target test signal data after noise reduction.

[0011] For the preprocessed target test signal data, a periodic window is set to intercept the periodic signal, and the intercepted periodic signal data is labelled to obtain the target periodic signal data;

[0012] Retrieve the target periodic signal data of the corresponding label, and extract the signal data at each time point in the period respectively; perform signal channel decomposition analysis based on the signal data at the corresponding time point, determine the number of decomposition layers by wavelet transform decomposition, perform channel characteristic component analysis and extraction on the signal data at the corresponding time point, and obtain the channel characteristic component set of the signal data at the corresponding time point; analyze the energy distribution data of the corresponding channel characteristic component based on the channel characteristic component data at the corresponding time point, and obtain the energy entropy data set in the corresponding channel component; wherein, the signal data wavelet transform decomposition analysis is calculated as follows:

[0013]

[0014] Where X(i, t) is the signal data corresponding to time point i in the corresponding numbered period t; j,n (i, t) is the energy distribution of the signal data corresponding to time point i in the corresponding number t period in the channel characteristic component n under the j-layer decomposition; is the wavelet basis function of the channel characteristic component n under the j-layer decomposition of the signal data corresponding to the time point i in the corresponding numbered t period; J is a constant;

[0015] Based on the energy distribution in the characteristic components of the signal data decomposition channel at each time point, the corresponding energy entropy in each channel component is analyzed, and the calculation formula is:

[0016]

[0017] Where E(j, n, i) is the energy entropy data of channel characteristic component n under j-layer decomposition in the signal data at the corresponding time point i; p j,n,i is the energy proportion of the channel characteristic component n under the j-layer decomposition in the signal data at the corresponding time point i; the energy proportion of the corresponding channel characteristic component p j,n,i The calculation formula is

[0018]

[0019] Based on the empirical mode decomposition of the characteristic components of each channel of the signal at the corresponding time point, the instantaneous characteristic frequency in each channel characteristic component is determined; the characteristic association group of the corresponding channel characteristic component is constructed by combining the energy entropy data and the instantaneous characteristic frequency data corresponding to the characteristic components of each channel at the signal at the corresponding time point; wherein, the analysis based on the empirical mode decomposition of the channel characteristic component is

[0020]

[0021] Wherein, imfn(i, t) is the IMF component of the characteristic component n of the signal data channel corresponding to the time point i in the corresponding number t period; r(i, t) is the remaining residual;

[0022] Combined with the IMF component in the corresponding channel characteristic component obtained by empirical mode decomposition, the instantaneous characteristic frequency analysis is performed, which is calculated as follows:

[0023]

[0024] Wherein, F(n, i, t) is the instantaneous characteristic frequency of the characteristic component n of the signal data channel corresponding to the time point i in the corresponding numbered t period; H(·) is the Hilbert transform.

[0025] Based on the characteristic association groups of the channel characteristic components corresponding to the signal data at each time point of the target periodic signal, the characteristic association groups of the signal data at each time point are analyzed respectively to determine the characterization association groups corresponding to the signal data at each time point; the analysis is as follows:

[0026] K(n,i,t)=E(j,n,i)*F(n,i,t);

[0027] Among them, K(n, i, t) is the characteristic association evaluation value of the signal data channel characteristic component n corresponding to the time point i in the period numbered t; based on the characteristic association evaluation values ​​of the channel characteristic components corresponding to the signal data at each time point of the corresponding periodic signal, the characteristic association group of the channel characteristic component corresponding to the maximum value is the representation association group of the signal data at the corresponding time point;

[0028] Based on the characterization association group of the signal data at each time point in the corresponding period, the characterization vector group of the signal data at each time point in the period is determined, and the characteristic state data of the signal data at each time point in the period is analyzed to obtain the characteristic state evaluation value of the signal data at each time point in the corresponding period; the analysis is as follows

[0029] Z(i,t)=K(n,i,t) z *cosα(n,i,t) z ;

[0030] Wherein, Z(i, t) is the characteristic state evaluation value of the signal data corresponding to time point i in the numbered t period; K(n, i, t)z is the characteristic association evaluation value of the characterization association group of the signal data channel characteristic component n corresponding to time point i in the numbered t period; α(n, i, t)z is the horizontal angle of the signal data curve corresponding to time point i in the numbered t period; the angle is taken as the angle between the line connecting the signal curve point corresponding to the current time point and the signal curve point at the next adjacent time point and the horizontal line;

[0031] Combined with the characteristic state evaluation value of the signal data at each time point in the cycle, the state steady-state analysis is performed on the target periodic signal data; the change difference value analysis of the signal curve data at adjacent time points is performed on the characteristic state evaluation value of the signal data at consecutive time points on the target periodic signal curve; combined with the change difference value analysis data of the signal curve data at consecutive time points on the target periodic signal curve, the periodic signal operation steady-state value of the target periodic data is analyzed; based on the periodic signal operation steady-state value analysis data of the target periodic signal data, the stability threshold is determined, and the target periodic signal of the current chip is judged to be abnormal; wherein, the steady-state value analysis of the target periodic signal is as follows:

[0032]

[0033] Wherein, W is the running steady-state value of the target periodic signal; Z(i+1, t) is the characteristic state evaluation value of the signal data at the next time point adjacent to the time point i on the target periodic signal curve; Q(i, i+1) is the curvature of the signal curve at the adjacent time point on the target periodic curve; Q(i, i+1)max and Q(i, i+1)min are the maximum and minimum curvatures of the signal curve at the adjacent time points on the target periodic curve, respectively; Z(i, t)max and Z(i, t)min are the maximum and minimum characteristic state evaluation values ​​of the signal data on the target periodic signal curve, respectively;

[0034] By introducing a steady-state threshold, if the running steady-state value of the target cycle signal is less than or equal to the threshold, it is judged that the current chip target cycle running state is good; otherwise, it is judged that the current chip target cycle running state is abnormal.

[0035] Output the corresponding signal curve data and the corresponding period signal operation steady-state value analysis data for each target period data divided by the chip test signal;

[0036] The output is based on the operation status judgment results of each target cycle chip, and the abnormal state chip and abnormal target cycle signal data are marked.

[0037] A chip test signal analysis and processing system based on big data, the system comprising a target signal acquisition module, a signal feature analysis module, a signal period steady-state analysis module and a chip data feedback module;

[0038] The target signal acquisition module acquires chip test signals, obtains target test signals through signal preprocessing, and sets a periodic window to retrieve target periodic signals from target test signals; the signal feature analysis module performs signal channel decomposition processing at corresponding periodic time points based on target periodic signals to obtain channel feature data of signal data at corresponding time points; the channel feature data includes channel feature components and instantaneous feature frequencies of corresponding channels; feature association groups of signal data at corresponding time points are constructed in combination with channel feature data; the signal periodic steady-state analysis module analyzes feature state data of signals at corresponding time points based on feature association groups corresponding to signal data at each time point within the period, and performs steady-state analysis of signal state operation at continuous time points within the period based on signal feature state data at each time point, and determines chip signal status based on analysis data; the chip data feedback module outputs periodic signal operation steady-state value analysis data of chip test signals and marks abnormal periodic signal data and abnormal state chips.

[0039] The target signal acquisition module includes a test signal acquisition unit and a target signal preprocessing unit;

[0040] The test signal acquisition unit is used to acquire test signal data generated by the chip operation;

[0041] The target signal preprocessing unit includes signal frequency band positioning processing and signal noise reduction processing;

[0042] The signal frequency band positioning processing is to use the signal acquisition equipment to perform signal sampling and debugging, adjust the sampling frequency according to the highest frequency of the chip test signal, and obtain the initial signal data of the target test signal in the target frequency range;

[0043] The signal noise reduction processing is to convert the collected initial signal data of the target test signal into the time domain and frequency domain, and remove the interference signal data in the target test signal through a filtering algorithm, and output the target test signal data after noise reduction.

[0044] The signal feature analysis module includes a signal channel component analysis unit and a signal channel feature association group construction unit;

[0045] The signal channel component analysis unit sets a period window for the preprocessed target test signal data to intercept the periodic signal, and performs labeling processing on the intercepted periodic signal data to obtain the target periodic signal data;

[0046] Retrieve the target periodic signal data of the corresponding label, and extract the signal data at each time point in the period respectively; perform signal channel decomposition analysis based on the signal data at the corresponding time point, determine the number of decomposition layers by wavelet transform decomposition, perform channel characteristic component analysis and extraction on the signal data at the corresponding time point, and obtain the channel characteristic component set of the signal data at the corresponding time point; analyze the energy distribution data of the corresponding channel characteristic component based on the channel characteristic component data at the corresponding time point, and obtain the energy entropy data set in the corresponding channel component;

[0047] The signal channel feature association group construction unit performs empirical mode decomposition based on the characteristic components of each channel of the signal at the corresponding time point to determine the instantaneous characteristic frequency in each channel characteristic component; and constructs a feature association group of the corresponding channel characteristic component by combining the energy entropy data and instantaneous characteristic frequency data corresponding to the characteristic components of each channel at the signal at the corresponding time point.

[0048] The signal period steady-state analysis module includes a signal characteristic state evaluation unit and a signal curve steady-state analysis unit;

[0049] The signal characteristic state evaluation unit analyzes the characteristic association groups of the signal data at each time point based on the characteristic association groups of the channel characteristic components corresponding to the signal data at each time point of the target periodic signal, and determines the characterization association groups corresponding to the signal data at each time point;

[0050] Based on the characterization association group of the signal data at each time point in the corresponding period, feature vectorization is performed to determine the characterization vector group of the signal data at each time point in the period, and the feature state data of the signal data at each time point in the period is analyzed to obtain the feature state evaluation value of the signal data at each time point in the corresponding period;

[0051] The signal curve steady-state analysis unit performs a steady-state analysis on the target periodic signal data in combination with the characteristic state evaluation values ​​of the signal data at each time point within the period; performs a change difference value analysis on the signal curve data at adjacent time points by analyzing the characteristic state evaluation values ​​of the signal data at consecutive time points on the target periodic signal curve; analyzes the periodic signal operation steady-state value of the target periodic data in combination with the change difference value analysis data of the signal curve data at consecutive time points on the target periodic signal curve; determines a stability threshold based on the periodic signal operation steady-state value analysis data of the target periodic signal data, and makes an abnormal judgment on the target periodic signal of the current chip.

[0052] The chip data feedback module includes a data feedback unit and an abnormal marking unit;

[0053] The data feedback unit outputs the corresponding signal curve data and the corresponding period signal operation steady-state value analysis data for each target period data divided by the chip test signal;

[0054] The abnormal marking unit outputs the judgment result of the operation state of each target cycle chip respectively, and marks the abnormal state chip and the abnormal target cycle signal data.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The present invention collects chip test signals and obtains target periodic signals by using target frequency band positioning and noise reduction processing; on this basis, channel characteristic components are determined by performing wavelet decomposition on signal data within the target period at corresponding time points, and the energy distribution and instantaneous characteristic frequency characteristics in each channel characteristic component are determined in turn, so as to establish a signal data characteristic association group at the corresponding time point; by determining the characterization association group for the signal data at each time point, the characteristic state value of the signal data at the corresponding time point is analyzed, and based on this, an operation steady-state analysis is performed on the target periodic signal curve to determine the operation state of the chip within the corresponding period; the present invention effectively analyzes the real-time operation state characteristics of the chip and determines the operation steady-state of the chip signal, and performs accurate measurement and analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a schematic diagram of the structure of a chip test signal analysis and processing system based on big data of the present invention;

[0058] Figure 2 The present invention is a flowchart of a chip test signal analysis and processing method based on big data. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Example: Figure 1 As shown, the present invention provides a technical solution:

[0061] A chip test signal analysis and processing system based on big data, the system comprising a target signal acquisition module, a signal feature analysis module, a signal period steady-state analysis module and a chip data feedback module;

[0062] The target signal acquisition module acquires chip test signals, obtains target test signals through signal preprocessing, and sets a periodic window to retrieve target periodic signals from target test signals; the signal feature analysis module performs signal channel decomposition processing at corresponding periodic time points based on target periodic signals to obtain channel feature data of signal data at corresponding time points; the channel feature data includes channel feature components and instantaneous feature frequencies of corresponding channels; feature association groups of signal data at corresponding time points are constructed in combination with channel feature data; the signal periodic steady-state analysis module analyzes feature state data of signals at corresponding time points based on feature association groups corresponding to signal data at each time point within the period, and performs steady-state analysis of signal state operation at continuous time points within the period based on signal feature state data at each time point, and determines chip signal status based on analysis data; the chip data feedback module outputs periodic signal operation steady-state value analysis data of chip test signals and marks abnormal periodic signal data and abnormal state chips.

[0063] The target signal acquisition module includes a test signal acquisition unit and a target signal preprocessing unit;

[0064] The test signal acquisition unit is used to acquire test signal data generated by the chip operation;

[0065] The target signal preprocessing unit includes signal frequency band positioning processing and signal noise reduction processing;

[0066] The signal frequency band positioning processing is to use the signal acquisition equipment to perform signal sampling and debugging, adjust the sampling frequency according to the highest frequency of the chip test signal, and obtain the initial signal data of the target test signal in the target frequency range;

[0067] The signal noise reduction processing is to convert the collected initial signal data of the target test signal into the time domain and frequency domain, and remove the interference signal data in the target test signal through a filtering algorithm, and output the target test signal data after noise reduction.

[0068] The signal feature analysis module includes a signal channel component analysis unit and a signal channel feature association group construction unit;

[0069] The signal channel component analysis unit sets a period window for the preprocessed target test signal data to intercept the periodic signal, and performs labeling processing on the intercepted periodic signal data to obtain the target periodic signal data;

[0070] Retrieve the target periodic signal data of the corresponding label, and extract the signal data at each time point in the period respectively; perform signal channel decomposition analysis based on the signal data at the corresponding time point, determine the number of decomposition layers by wavelet transform decomposition, perform channel characteristic component analysis and extraction on the signal data at the corresponding time point, and obtain the channel characteristic component set of the signal data at the corresponding time point; analyze the energy distribution data of the corresponding channel characteristic component based on the channel characteristic component data at the corresponding time point, and obtain the energy entropy data set in the corresponding channel component;

[0071] The signal channel feature association group construction unit performs empirical mode decomposition based on the characteristic components of each channel of the signal at the corresponding time point to determine the instantaneous characteristic frequency in each channel characteristic component; and constructs a feature association group of the corresponding channel characteristic component by combining the energy entropy data and instantaneous characteristic frequency data corresponding to the characteristic components of each channel at the signal at the corresponding time point.

[0072] The signal period steady-state analysis module includes a signal characteristic state evaluation unit and a signal curve steady-state analysis unit;

[0073] The signal characteristic state evaluation unit analyzes the characteristic association groups of the signal data at each time point based on the characteristic association groups of the channel characteristic components corresponding to the signal data at each time point of the target periodic signal, and determines the characterization association groups corresponding to the signal data at each time point;

[0074] Based on the characterization association group of the signal data at each time point in the corresponding period, feature vectorization is performed to determine the characterization vector group of the signal data at each time point in the period, and the feature state data of the signal data at each time point in the period is analyzed to obtain the feature state evaluation value of the signal data at each time point in the corresponding period;

[0075] The signal curve steady-state analysis unit performs a steady-state analysis on the target periodic signal data in combination with the characteristic state evaluation values ​​of the signal data at each time point within the period; performs a change difference value analysis on the signal curve data at adjacent time points by analyzing the characteristic state evaluation values ​​of the signal data at consecutive time points on the target periodic signal curve; analyzes the periodic signal operation steady-state value of the target periodic data in combination with the change difference value analysis data of the signal curve data at consecutive time points on the target periodic signal curve; determines a stability threshold based on the periodic signal operation steady-state value analysis data of the target periodic signal data, and makes an abnormal judgment on the target periodic signal of the current chip.

[0076] The chip data feedback module includes a data feedback unit and an abnormal marking unit;

[0077] The data feedback unit outputs the corresponding signal curve data and the corresponding period signal operation steady-state value analysis data for each target period data divided by the chip test signal;

[0078] The abnormal marking unit outputs the operation status judgment result of each target cycle chip respectively, and marks the abnormal state chip and abnormal target cycle signal data;

[0079] like Figure 2 As shown, the present invention provides another technical solution:

[0080] A chip test signal analysis and processing method based on big data, the method comprising the following steps:

[0081] Collect chip test signals, obtain target test signals through signal preprocessing, and set a periodic window to retrieve target periodic signals from the target test signals; the signal preprocessing includes signal frequency band positioning processing and signal noise reduction processing;

[0082] Based on the target periodic signal, the signal channel of the corresponding periodic time point is decomposed and processed to obtain the channel characteristic data of the signal data at the corresponding time point; the channel characteristic data includes the channel characteristic component and the instantaneous characteristic frequency of the corresponding channel; the characteristic association group of the signal data at the corresponding time point is constructed in combination with the channel characteristic data, and based on the characteristic association group corresponding to the signal data at each time point in the period, the characteristic state data of the signal at the corresponding time point is analyzed, and based on the signal characteristic state data at each time point, the signal state operation steady-state analysis of continuous time points in the period is performed, and the chip signal state is determined based on the analysis data.

[0083] The signal frequency band positioning processing is to use the signal acquisition equipment to perform signal sampling and debugging, adjust the sampling frequency according to the highest frequency of the chip test signal, and obtain the initial signal data of the target test signal in the target frequency range;

[0084] The signal noise reduction processing is to convert the collected initial signal data of the target test signal into the time domain and frequency domain, and remove the interference signal data in the target test signal through a filtering algorithm, and output the target test signal data after noise reduction.

[0085] For the preprocessed target test signal data, a periodic window is set to intercept the periodic signal, and the intercepted periodic signal data is labelled to obtain the target periodic signal data;

[0086] Retrieve the target periodic signal data of the corresponding label, and extract the signal data at each time point in the period respectively; perform signal channel decomposition analysis based on the signal data at the corresponding time point, determine the number of decomposition layers by wavelet transform decomposition, perform channel characteristic component analysis and extraction on the signal data at the corresponding time point, and obtain the channel characteristic component set of the signal data at the corresponding time point; analyze the energy distribution data of the corresponding channel characteristic component based on the channel characteristic component data at the corresponding time point, and obtain the energy entropy data set in the corresponding channel component; wherein, the signal data wavelet transform decomposition analysis is calculated as follows:

[0087]

[0088] Where X(i, t) is the signal data corresponding to time point i in the corresponding numbered period t; j,n (i, t) is the energy distribution of the signal data corresponding to time point i in the corresponding number t period in the channel characteristic component n under the j-layer decomposition; is the wavelet basis function of the channel characteristic component n under the j-layer decomposition of the signal data corresponding to the time point i in the corresponding numbered t period; J is a constant;

[0089] Based on the energy distribution in the characteristic components of the signal data decomposition channel at each time point, the corresponding energy entropy in each channel component is analyzed, and the calculation formula is:

[0090]

[0091] Where E(j, n, i) is the energy entropy data of channel characteristic component n under j-layer decomposition in the signal data at the corresponding time point i; p j,n,i is the energy proportion of the channel characteristic component n under the j-layer decomposition in the signal data at the corresponding time point i; the energy proportion of the corresponding channel characteristic component p j,n,i The calculation formula is

[0092]

[0093] Based on the empirical mode decomposition of the characteristic components of each channel of the signal at the corresponding time point, the instantaneous characteristic frequency in each channel characteristic component is determined; the characteristic association group of the corresponding channel characteristic component is constructed by combining the energy entropy data and the instantaneous characteristic frequency data corresponding to the characteristic components of each channel at the signal at the corresponding time point; wherein, the analysis based on the empirical mode decomposition of the channel characteristic component is

[0094]

[0095] Wherein, imfn(i, t) is the IMF component of the characteristic component n of the signal data channel corresponding to the time point i in the corresponding number t period; r(i, t) is the remaining residual;

[0096] Combined with the IMF component in the corresponding channel characteristic component obtained by empirical mode decomposition, the instantaneous characteristic frequency analysis is performed, which is calculated as follows:

[0097]

[0098] Wherein, F(n, i, t) is the instantaneous characteristic frequency of the characteristic component n of the signal data channel corresponding to the time point i in the corresponding numbered t period; H(·) is the Hilbert transform.

[0099] Based on the characteristic association groups of the channel characteristic components corresponding to the signal data at each time point of the target periodic signal, the characteristic association groups of the signal data at each time point are analyzed respectively to determine the characterization association groups corresponding to the signal data at each time point; the analysis is as follows:

[0100] K(n,i,t)=E(j,n,i)*F(n,i,t);

[0101] Among them, K(n, i, t) is the characteristic association evaluation value of the signal data channel characteristic component n corresponding to the time point i in the period numbered t; based on the characteristic association evaluation values ​​of the channel characteristic components corresponding to the signal data at each time point of the corresponding periodic signal, the characteristic association group of the channel characteristic component corresponding to the maximum value is the representation association group of the signal data at the corresponding time point;

[0102] Based on the characterization association group of the signal data at each time point in the corresponding period, the characterization vector group of the signal data at each time point in the period is determined, and the characteristic state data of the signal data at each time point in the period is analyzed to obtain the characteristic state evaluation value of the signal data at each time point in the corresponding period; the analysis is as follows

[0103] Z(i,t)=K(n,i,t) z *cosα(n,i,t) z ;

[0104] Wherein, Z(i, t) is the characteristic state evaluation value of the signal data corresponding to time point i in the numbered t period; K(n, i, t)z is the characteristic association evaluation value of the characterization association group of the signal data channel characteristic component n corresponding to time point i in the numbered t period; α(n, i, t)z is the horizontal angle of the signal data curve corresponding to time point i in the numbered t period; the angle is taken as the angle between the line connecting the signal curve point corresponding to the current time point and the signal curve point at the next adjacent time point and the horizontal line;

[0105] Combined with the characteristic state evaluation value of the signal data at each time point in the cycle, the state steady-state analysis is performed on the target periodic signal data; the change difference value analysis of the signal curve data at adjacent time points is performed on the characteristic state evaluation value of the signal data at consecutive time points on the target periodic signal curve; combined with the change difference value analysis data of the signal curve data at consecutive time points on the target periodic signal curve, the periodic signal operation steady-state value of the target periodic data is analyzed; based on the periodic signal operation steady-state value analysis data of the target periodic signal data, the stability threshold is determined, and the target periodic signal of the current chip is judged to be abnormal; wherein, the steady-state value analysis of the target periodic signal is as follows:

[0106]

[0107] Wherein, W is the running steady-state value of the target periodic signal; Z(i+1, t) is the characteristic state evaluation value of the signal data at the next time point adjacent to the time point i on the target periodic signal curve; Q(i, i+1) is the curvature of the signal curve at the adjacent time point on the target periodic curve; Q(i, i+1)max and Q(i, i+1)min are the maximum and minimum curvatures of the signal curve at the adjacent time points on the target periodic curve, respectively; Z(i, t)max and Z(i, t)min are the maximum and minimum characteristic state evaluation values ​​of the signal data on the target periodic signal curve, respectively;

[0108] By introducing a steady-state threshold, if the running steady-state value of the target cycle signal is less than or equal to the threshold, it is judged that the current chip target cycle running state is good; otherwise, it is judged that the current chip target cycle running state is abnormal.

[0109] Output the corresponding signal curve data and the corresponding period signal operation steady-state value analysis data for each target period data divided by the chip test signal;

[0110] The output is based on the operation status judgment results of each target cycle chip, and the abnormal state chip and abnormal target cycle signal data are marked.

[0111] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A chip test signal analysis and processing method based on big data, characterized in that: The method comprises the following steps: Collect chip test signals, obtain target test signals through signal preprocessing, and set a periodic window to retrieve target periodic signals from the target test signals; the signal preprocessing includes signal frequency band positioning processing and signal noise reduction processing; Based on the target periodic signal, the signal channel of the corresponding periodic time point is decomposed and processed to obtain the channel characteristic data of the signal data at the corresponding time point; the channel characteristic data includes the channel characteristic component and the instantaneous characteristic frequency of the corresponding channel; the characteristic association group of the signal data at the corresponding time point is constructed in combination with the channel characteristic data, and based on the characteristic association group corresponding to the signal data at each time point in the period, the characteristic state data of the signal at the corresponding time point is analyzed, and based on the signal characteristic state data at each time point, the signal state operation steady-state analysis of continuous time points in the period is performed, and the chip signal state is determined based on the analysis data.

2. The chip test signal analysis and processing method based on big data according to claim 1, characterized in that: The signal frequency band positioning processing is to use the signal acquisition equipment to perform signal sampling and debugging, adjust the sampling frequency according to the highest frequency of the chip test signal, and obtain the initial signal data of the target test signal in the target frequency range; The signal noise reduction processing is to convert the collected initial signal data of the target test signal into the time domain and frequency domain, and remove the interference signal data in the target test signal through a filtering algorithm, and output the target test signal data after noise reduction.

3. The chip test signal analysis and processing method based on big data according to claim 2 is characterized in that: For the preprocessed target test signal data, a periodic window is set to intercept the periodic signal, and the intercepted periodic signal data is labelled to obtain the target periodic signal data; Retrieve the target period signal data with the corresponding label, and extract the signal data at each time point in the period respectively; Perform signal channel decomposition analysis based on the signal data at the corresponding time point, use wavelet transform decomposition to determine the number of decomposition layers, perform channel feature component analysis and extraction on the signal data at the corresponding time point, and obtain a channel feature component set of the signal data at the corresponding time point; Analyze the energy distribution data of the corresponding channel characteristic component based on the channel characteristic component data at the corresponding time point to obtain the energy entropy data set in the corresponding channel component; Based on the empirical mode decomposition of the characteristic components of each channel of the signal at the corresponding time point, the instantaneous characteristic frequency in the characteristic components of each channel is determined; the characteristic association group of the corresponding channel characteristic components is constructed by combining the energy entropy data and the instantaneous characteristic frequency data corresponding to the characteristic components of each channel at the signal at the corresponding time point.

4. The chip test signal analysis and processing method based on big data according to claim 3 is characterized in that: Based on the characteristic association groups of the channel characteristic components corresponding to the signal data at each time point of the target periodic signal, the characteristic association groups of the signal data at each time point are analyzed respectively to determine the characterization association groups corresponding to the signal data at each time point; Based on the characterization association group of the signal data at each time point in the corresponding period, feature vectorization is performed to determine the characterization vector group of the signal data at each time point in the period, and the feature state data of the signal data at each time point in the period is analyzed to obtain the feature state evaluation value of the signal data at each time point in the corresponding period; Combined with the characteristic state evaluation values ​​of the signal data at each time point within the cycle, a state steady-state analysis is performed on the target periodic signal data; the change difference value analysis of the signal curve data at adjacent time points is performed on the characteristic state evaluation values ​​of the signal data at consecutive time points on the target periodic signal curve; combined with the change difference value analysis data of the signal curve data at consecutive time points on the target periodic signal curve, the periodic signal operation steady-state value of the target periodic data is analyzed; based on the periodic signal operation steady-state value analysis data of the target periodic signal data, a stability threshold is determined, and an abnormal judgment is made on the target periodic signal of the current chip.

5. The chip test signal analysis and processing method based on big data according to claim 4 is characterized in that: Output the corresponding signal curve data and the corresponding period signal operation steady-state value analysis data for each target period data divided by the chip test signal; The output is based on the operation status judgment results of each target cycle chip, and the abnormal state chip and abnormal target cycle signal data are marked.

6. A chip test signal analysis and processing system based on big data, characterized in that: The system includes a target signal acquisition module, a signal feature analysis module, a signal period steady-state analysis module and a chip data feedback module; The target signal acquisition module acquires chip test signals, obtains target test signals through signal preprocessing, and sets a periodic window to retrieve target periodic signals from target test signals; the signal feature analysis module performs signal channel decomposition processing at corresponding periodic time points based on target periodic signals to obtain channel feature data of signal data at corresponding time points; the channel feature data includes channel feature components and instantaneous feature frequencies of corresponding channels; feature association groups of signal data at corresponding time points are constructed in combination with channel feature data; the signal periodic steady-state analysis module analyzes feature state data of signals at corresponding time points based on feature association groups corresponding to signal data at each time point within the period, and performs steady-state analysis of signal state operation at continuous time points within the period based on signal feature state data at each time point, and determines chip signal status based on analysis data; the chip data feedback module outputs periodic signal operation steady-state value analysis data of chip test signals and marks abnormal periodic signal data and abnormal state chips.

7. The chip test signal analysis and processing system based on big data according to claim 6, characterized in that: The target signal acquisition module includes a test signal acquisition unit and a target signal preprocessing unit; The test signal acquisition unit is used to acquire test signal data generated by the chip operation; The target signal preprocessing unit includes signal frequency band positioning processing and signal noise reduction processing; The signal frequency band positioning processing is to use the signal acquisition equipment to perform signal sampling and debugging, adjust the sampling frequency according to the highest frequency of the chip test signal, and obtain the initial signal data of the target test signal in the target frequency range; The signal noise reduction processing is to convert the collected initial signal data of the target test signal into the time domain and frequency domain, and remove the interference signal data in the target test signal through a filtering algorithm, and output the target test signal data after noise reduction.

8. The chip test signal analysis and processing system based on big data according to claim 7, characterized in that: The signal feature analysis module includes a signal channel component analysis unit and a signal channel feature association group construction unit; The signal channel component analysis unit sets a period window for the preprocessed target test signal data to intercept the periodic signal, and performs labeling processing on the intercepted periodic signal data to obtain the target periodic signal data; Retrieve the target period signal data with the corresponding label, and extract the signal data at each time point in the period respectively; Perform signal channel decomposition analysis based on the signal data at the corresponding time point, use wavelet transform decomposition to determine the number of decomposition layers, perform channel feature component analysis and extraction on the signal data at the corresponding time point, and obtain a channel feature component set of the signal data at the corresponding time point; Analyze the energy distribution data of the corresponding channel characteristic component based on the channel characteristic component data at the corresponding time point to obtain the energy entropy data set in the corresponding channel component; The signal channel feature association group construction unit performs empirical mode decomposition based on the characteristic components of each channel of the signal at the corresponding time point to determine the instantaneous characteristic frequency in each channel characteristic component; and constructs a feature association group of the corresponding channel characteristic component by combining the energy entropy data and instantaneous characteristic frequency data corresponding to the characteristic components of each channel at the signal at the corresponding time point.

9. The chip test signal analysis and processing system based on big data according to claim 8, characterized in that: The signal period steady-state analysis module includes a signal characteristic state evaluation unit and a signal curve steady-state analysis unit; The signal characteristic state evaluation unit analyzes the characteristic association groups of the signal data at each time point based on the characteristic association groups of the channel characteristic components corresponding to the signal data at each time point of the target periodic signal, and determines the characterization association groups corresponding to the signal data at each time point; Based on the characterization association group of the signal data at each time point in the corresponding period, feature vectorization is performed to determine the characterization vector group of the signal data at each time point in the period, and the feature state data of the signal data at each time point in the period is analyzed to obtain the feature state evaluation value of the signal data at each time point in the corresponding period; The signal curve steady-state analysis unit performs a steady-state analysis on the target periodic signal data in combination with the characteristic state evaluation values ​​of the signal data at each time point within the period; performs a change difference value analysis on the signal curve data at adjacent time points by analyzing the characteristic state evaluation values ​​of the signal data at consecutive time points on the target periodic signal curve; analyzes the periodic signal operation steady-state value of the target periodic data in combination with the change difference value analysis data of the signal curve data at consecutive time points on the target periodic signal curve; determines a stability threshold based on the periodic signal operation steady-state value analysis data of the target periodic signal data, and makes an abnormal judgment on the target periodic signal of the current chip.

10. The chip test signal analysis and processing system based on big data according to claim 9, characterized in that: The chip data feedback module includes a data feedback unit and an abnormal marking unit; The data feedback unit outputs the corresponding signal curve data and the corresponding period signal operation steady-state value analysis data for each target period data divided by the chip test signal; The abnormal marking unit outputs the judgment result of the operation state of each target cycle chip respectively, and marks the abnormal state chip and the abnormal target cycle signal data.

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