CN115047022B - Time domain reconstruction method and system for thermal diffusion process - Google Patents
Time domain reconstruction method and system for thermal diffusion process Download PDFInfo
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Abstract
The invention provides a time domain reconstruction method and a time domain reconstruction system in a thermal diffusion process, wherein the method comprises the following steps: applying an excitation signal to a sample, and emitting an infrared signal at a sample test point; collecting infrared signals of the sample test point, and calculating to obtain delay phase information based on the collected infrared signals; performing Fourier expansion on the periodic function of the excitation signal to construct a prediction function of the infrared signal intensity value of the sample test point; determining an amplitude parameter and a phase parameter of the prediction function based on the infrared signal of the sample test point and corresponding delay phase information; and sampling the sample test point according to the prediction function to obtain a thermal diffusion image of the sample test point. The invention can solve the defects that the detection technology of the phase-locked thermal infrared imager is easily interfered by external noise and is not easy to interpret.
Description
Technical Field
The invention relates to the field of image processing, in particular to a time domain reconstruction method and a time domain reconstruction system for a thermal diffusion process.
Background
In the information age, electronic products have been widely used. The electronic product itself is composed of electronic components such as a logic operation device (CPU), a memory device (magnetic disk), and the like. In the production process of these electronic components and after a failure occurs in use for a certain period of time, it is necessary to detect them, and therefore, a method of detecting a failure of these electronic components is very important.
In recent years, nondestructive detection means are relatively simple and efficient, and a phase-locked thermal infrared imager measurement method is taken as a main representative. The phase-locked thermal infrared imager detection technology adopts an exciting source to heat a tested piece, observes and records the infrared signal change on the surface of a material through a thermal infrared imager, and converts the infrared signal change into a visible temperature image, thereby judging and detecting the defects of a sample.
However, the phase-locked thermal infrared imager detection technology in the prior art is easily interfered by internal and external noise, so that the infrared flaw detection image has low contrast, high noise content and the like, and is difficult to interpret.
Disclosure of Invention
The technical problem solved by the technical scheme of the invention is as follows: the method solves the problems that the phase-locked thermal infrared imager detection technology is easily interfered by external noise and is difficult to interpret.
In order to solve the above technical problem, a technical solution of the present invention provides a time domain reconstruction method for a thermal diffusion process, including:
applying an excitation signal to a sample, wherein the excitation signal is a periodic signal, and the sample is excited by the excitation signal to emit an infrared signal at a sample test point;
collecting infrared signals of the sample test point, and calculating to obtain delay phase information based on the collected infrared signals;
performing Fourier expansion on the periodic function of the excitation signal, and constructing a prediction function of the infrared signal intensity value of the sample test point, wherein the prediction function comprises an amplitude parameter and a phase parameter;
determining an amplitude parameter and a phase parameter of the prediction function based on the periodic infrared signal of the sample test point and the delay phase information;
and sampling the sample test point according to the prediction function to obtain a thermal diffusion image of the sample test point.
Optionally, the excitation signal is a square wave signal, and the fourier expansion of the periodic function of the excitation signal includes:
fourier expansion is performed on the periodic function of the excitation signal based on the waveform of the square wave signal.
Optionally, the fourier expanding the periodic function of the excitation signal includes:
expressing the periodic function based on a trigonometric function; alternatively, the first and second liquid crystal display panels may be,
the periodic function is expressed based on a combination of a trigonometric function and a trigonometric function.
Optionally, the prediction function is:
wherein t is a time, A is the amplitude parameter,
for the purpose of said phase parameter(s),
in the form of a circular frequency, the frequency of the circular frequency,
，f c is the phase locked frequency.
Optionally, the prediction function is:
wherein t is time, C is a constant,
for the parameters of the higher order terms corresponding to the fourier expansion,
for the amplitude parameter corresponding to the higher order term parameter,
for the phase parameter corresponding to the higher order term,
is the phase-locked frequency.
Optionally, the prediction function is:
wherein t is a time, C is a constant,
for the high order term parameters corresponding to the fourier expansion,
is the maximum amplitude parameter of the excitation signal waveform,
is the excitation signal waveform duty cycle parameter,
in the form of a circular frequency, the frequency of the circular frequency,
，f c is the phase locked frequency.
Optionally, the acquiring the infrared signal and the corresponding delay phase information of the sample test point includes:
and carrying out single measurement on the sample test point through a phase-locked infrared camera, and determining the infrared signal and the corresponding delay phase information of the sample test point.
Optionally, the sampling the sample test point according to the prediction function to obtain the thermal diffusion image of the sample test point includes:
setting the thermal diffusion image time required to be acquired in the period based on the period of the excitation signal;
and generating a thermal diffusion image of the corresponding sample test point at each moment according to the prediction function.
Optionally, the sampling the sample test point according to the prediction function to obtain the thermal diffusion image of the sample test point includes:
determining a sample sampling period according to the heating starting time of the sample test point;
setting the thermal diffusion image time required to be acquired in the period based on the sampling period;
and generating a thermal diffusion image of the corresponding sample test point at each moment according to the prediction function.
Optionally, the time domain reconstructing method further includes:
and synthesizing a thermal diffusion dynamic image of the sample based on the thermal diffusion image at the corresponding moment of the sample test point.
Optionally, the sample is an MOS tube, an IGBT tube, or a wire.
In order to solve the above technical problem, the technical solution of the present invention further provides a time domain reconstruction system for a thermal diffusion process, including:
the excitation unit is suitable for applying an excitation signal to a sample, the excitation signal is a periodic signal, and the sample is excited by the excitation signal to emit an infrared signal at a sample test point;
the acquisition unit is suitable for acquiring infrared signals of the sample test point;
the computing unit is suitable for computing delay phase information based on the collected infrared signals;
the construction unit is suitable for performing Fourier expansion on the periodic function of the excitation signal and constructing a prediction function of the infrared signal intensity value of the sample test point, wherein the prediction function comprises an amplitude parameter and a phase parameter;
the determining unit is suitable for determining the amplitude parameter and the phase parameter of the prediction function based on the infrared signal of the sample test point and the delay phase information;
and the sampling unit is suitable for sampling the sample test point according to the prediction function so as to obtain a thermal diffusion image of the sample test point.
Optionally, the time domain reconstruction system of the thermal diffusion process further includes:
and the synthesis unit is suitable for synthesizing the thermal diffusion dynamic image of the sample based on the thermal diffusion image at the corresponding moment of the sample test point.
The technical scheme of the invention at least comprises the following beneficial effects:
the technical scheme of the invention can collect the infrared signals and the corresponding delay phase information of the sample test points based on the phase-locked thermal infrared imager detection technology, construct a prediction function of the infrared signal intensity value of the sample test points through the Fourier expansion of excitation signal waves, determine the amplitude parameters and the phase parameters of the prediction function through the collected infrared signals and the corresponding delay phase information, and sample the sample test points based on the constructed prediction function to obtain the thermal diffusion images of the sample test points; through the construction of the prediction function and the re-sampling of the test points, the defects that a phase-locked thermal infrared imager detection sample is easily interfered by external noise and is difficult to interpret in the prior art can be overcome.
The technical scheme of the invention can effectively restore the process that the temperature distribution on the sample dynamically changes along with the time, can obtain the time domain reconstruction result by applying the excitation signal with fixed frequency to carry out single phase-locked infrared measurement without multiple measurements, and has the advantages of simple, rapid and reliable measurement method, visual and concise measurement result and the like.
According to the technical scheme, fourier expansion is carried out on the waveform of the excitation signal, a prediction function based on the infrared signal intensity of the sample test point is constructed, and the algorithm is simple and reliable, requires a small storage space and has the advantage of low cost.
In an optional technical scheme of the technical scheme, the invention further provides a scheme for measuring and sampling the test points at different positions of the sample. Due to the fact that time sequences of hot spots of test points at different positions of the sample are different, the technical scheme of the invention can periodically reflect the heating sequence of the different positions of the sample, so that the conduction process of the hot spots in the sample can be dynamically presented, great convenience is brought to detection and analysis of subsequent data, and material properties of different areas on an electronic device and depth information of fault positions in the device are further represented.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
fig. 1 is a schematic flow chart illustrating steps of a time domain reconstruction method for a thermal diffusion process according to an embodiment of the present invention;
FIG. 2 is a schematic waveform diagram of an excitation signal provided by the present invention;
FIG. 3 is a schematic structural diagram of a collection and computer system for applying an excitation signal to a sample according to an embodiment of the present invention;
fig. 4 is a schematic flow chart illustrating steps of a time domain reconstruction method for another thermal diffusion process according to the present invention;
FIG. 5 is a schematic diagram of a periodic temperature profile for time domain reconstruction in a thermal diffusion process of an MOS transistor according to the present invention;
FIG. 6 is a schematic flow chart of an algorithm for synthesizing an image of a sample in a period containing a high-order term according to the present invention;
fig. 7 is a schematic diagram of a periodic temperature distribution image for time domain reconstruction of an IGBT tube thermal diffusion process according to the technical solution of the present invention;
fig. 8 is a schematic diagram of a periodic temperature distribution image for time domain reconstruction of a thermal diffusion process of a nickel-titanium alloy wire according to the technical solution of the present invention.
Detailed Description
In order to better and clearly show the technical scheme of the invention, the invention is further described with reference to the attached drawings.
The phase-locked thermal infrared imager detection technology can apply periodic excitation signals to electronic components in the production process or the use process, a periodic heat source is formed on the electronic components to be detected, and images related to the heating process of the electronic components to be detected are obtained through image processing of the acquired infrared signals. According to the technical scheme, the time domain reconstruction is carried out on the heating process of the sample in a period by measuring the intensity information and the delay phase information of the infrared image of the periodically heated sample, the relation between the temperature distribution on the sample and the time in the period is displayed, and the thermal diffusion image of the sample test point is formed.
The present embodiment provides a time domain reconstruction method of a thermal diffusion process as shown in fig. 1, which includes the following steps:
step S100, applying an excitation signal to a sample, wherein the excitation signal is a periodic signal, and the sample is excited by the excitation signal to emit an infrared signal at a sample test point.
In this step, the sample may be an electronic component, such as an MOS transistor, an IGBT transistor, or a metal wire, during the production process or during the use process.
In theory, the excitation signal may be any periodic signal suitable for the embodiment of the present invention, and may be conveniently and appropriately selected to be a square wave signal. Fig. 2 illustrates that three square wave signals with different duty ratios (80%, 50%, 20% square wave signals, respectively) can be used as the excitation signal waveform of step S100.
Referring to fig. 3, when an excitation signal is applied to a sample, test points at different positions of the sample are excited by a current (i.e., the excitation signal), and an infrared signal is generated. An infrared camera can be used to obtain the infrared signals of the various test points of the sample. It should be noted that fig. 3 only shows that the excitation signal is a current by way of example, but in practice, the excitation sources such as voltage, radiation, stress, etc. are all replaceable and usable, and the present embodiment does not limit the specific signal form of the excitation signal.
The infrared camera obtains the heating condition of each test point, and the heating condition is different due to different positions of the test points. The test point may be a sample position corresponding to a sample pixel point in the image corresponding to the image generated by the infrared camera.
And S101, acquiring infrared signals of the sample test point, and calculating to obtain delay phase information based on the acquired infrared signals.
With continued reference to fig. 3, an infrared camera may be used to acquire infrared signals collected at various test points of the sample. Because the excitation signal is periodic, a periodic heat source is formed on each test point of the sample, and the infrared signal intensity and the delay phase information of the heating position on the sample are calculated through image processing of the collected infrared signal. The infrared camera has a synchronization process with the excitation source (schematically shown as current in fig. 3) at the time of acquisition, thereby generating and obtaining delayed phase information of the infrared signal.
And S102, performing Fourier expansion on the periodic function of the excitation signal, and constructing a prediction function of the infrared signal intensity value of the sample test point, wherein the prediction function comprises an amplitude parameter and a phase parameter.
In step S102, any periodic function can be fourier-expanded, so that fourier expansion can be used for the periodic function expression of the excitation signal, that is, the periodic waveform of the excitation signal is described by a trigonometric series, and a prediction function of the infrared signal intensity value of the sample test point is constructed by the infrared signal intensity of the test point and the corresponding delay phase information obtained in step S101. Further, the prediction function may be obtained by calculating a functional expression in which the infrared signal intensity value of each test point of the sample periodically changes with time. Specifically, a prediction function of the sample test point can be constructed by using a trigonometric function and a periodic amplitude value and a delayed phase value of the infrared signal intensity value of the corresponding test point based on the fourier expansion of the excitation signal periodic function.
In the present embodiment, the prediction function may be a trigonometric function simulating a change in signal intensity with time according to the waveform of the excitation signal. Since the prediction function is expressed by fourier expansion based on the waveform of the excitation signal, an arbitrary trigonometric function expression and a combination of trigonometric functions may be used, such as a sin function may be typically applicable. Other forms of trigonometric functions, such as cos functions, tan · cos functions and combinations of other trigonometric functions, may also be used, essentially identical to the result obtained with sin functions without any distinction. In the case that the excitation signal is a square wave signal, the fourier transform theory proves that any periodic square wave function can be represented by the sum of a series of trigonometric functions, and therefore the construction method of the prediction function under the square wave signal is the same.
Specifically, the prediction function may be:
Specifically, the prediction function may also consider a higher-order term of fourier expansion of the waveform of the test point excitation signal, so that the following function may also be applied as the prediction function model:
Specifically, the prediction function may also consider the duty ratio of the excitation signal waveform in consideration of a high-order term of fourier expansion of the excitation signal waveform of the test point, and in combination with fig. 2, in the case of excitation signal waveforms with different duty ratios, a duty ratio parameter may be added to the prediction function to improve the accuracy of the prediction model, so that the following function may also be applied to the prediction function model of this embodiment:
Step S103, determining the amplitude parameter and the phase parameter of the prediction function based on the infrared signal of the sample test point and the corresponding delay phase information.
In step S103, based on the prediction function constructed in step S102, a single measurement may be performed on each sample test point by using the phase-locked infrared camera, and the infrared signal and the corresponding delay phase information of the sample test point are determined, so as to determine the parameters in the prediction function.
For example, for predictive functions
：
If the excitation signal introduced into the sample is set as a fixed frequency
The square wave current can obtain the amplitude A and the delay phase value of the infrared signal intensity of each test point on the sample measured by the phase-locked infrared camera
(ii) a At this time, the known circular frequency is
，
，f c Is an infrared camera phase lockFrequency, a function expression of the periodic change of the infrared signal intensity value of each test point in the sample along with the time can be calculated, namely
。
As another example, for predictive functions
：
Prediction function
The process of more finely reducing the sample heating, many higher order terms of the fourier expansion are also taken into account. By performing a single measurement with a phase-locked infrared camera, it can be determined in turn
A in (1) 1 ，A 2 ，A 3 8230; (i.e. it is made of
) And
，
，
8230; (i.e. it is made of
) And (5) waiting for parameters, and obtaining a prediction function parameter value of the pixel point in the infrared image of the sample along with the change of time.
As another example, for a prediction function
：
Prediction function
The waveform duty cycle of the sample test point excitation signal is also considered, and the waveform duty cycle parameters are known. In fact, the prediction function
Based on predictive functions
And the waveform duty cycle is taken into account. Prediction function
Is
Is composed of
，
Is composed of
Constructed as a prediction function
。
For square wave amplitude, to be identifiable by a single measurement by an infrared camera
，
Carry-in-back prediction function
Can be reduced to obtain a signal with any duty ratio to obtain a prediction function
The parameter (c) of (c). Prediction function
The duty cycle of (2) is adjustable. By passing
The parameters adjust the waveform duty ratio parameters, the duty ratio is large,
the value is large and ranges from 0 to 100 percent. At the moment, the signal frequency is unchanged, and the power of the excitation signal can be adjusted through the duty ratio parameter. For example, the excitation signal in FIG. 2 is a square wave signal corresponding to a duty cycle parameter of 80% of the square wave signal
Is 80%, corresponding to the duty ratio parameter of 50% square wave signal
Is 50%, corresponding to the duty ratio parameter of 20% square wave signal
The content was 20%.
And step S104, sampling the sample test point according to the prediction function to obtain a thermal diffusion image of the sample test point.
Based on step S104, with reference to fig. 3, according to the calculated prediction function, an image integer to be presented by the sample test point in the next period of the excitation signal may be preset by the computer, and the computer is configured to generate the thermal diffusion image of the test point in the period based on the prediction function.
Specifically, the sampling the sample test point according to the prediction function to obtain the thermal diffusion image of the sample test point includes:
setting the thermal diffusion image time required to be acquired in the period based on the period of applying the excitation signal to the sample test point;
and generating a thermal diffusion image of the corresponding sample test point at each moment according to the prediction function.
According to the process, the method can correspond to a computer to generate pixel points in the thermal diffusion image of the sample test point, the pixel points set the number of the image frames to be presented in one period of the sample test point to be N according to the heating process of the prediction function in the period, and the N is a natural number which is larger than or equal to zero.
For prediction functions
And setting i as an index value for generating a sample thermal diffusion image, wherein the image comprises pixel values of a plurality of sample test points, and i is a natural number which is greater than or equal to zero and less than or equal to N. The pixel value of the sample thermal diffusion image can be determined by the following function
Obtaining:
Of course, for the prediction function
Or taking values at N moments in a period and predicting a function
And acquiring the image pixel value at the temperature distribution value at each moment. If a heating period is T =0.53s, a temperature distribution image with T at the time of 0s,0.11s 0.21s,0.32s,0.43s,0.53s may be taken.
For other prediction function models, the image dereferencing process is similar, and is not described herein again.
Because the sample test points are multiple and can correspond to pixel points in a computer image, each pixel point has a heating process in a period according to a prediction function, and the heating process of each test point (which also corresponds to a pixel point in a computer) has different heating starting realization according to different test point positions, sampling the sample test points according to the prediction function to obtain the thermal diffusion image of the sample test points can also comprise:
determining a sample sampling period according to the heating starting time of the sample test point;
setting the thermal diffusion image time required to be acquired in the period based on the heating period of the sample test point;
and generating a thermal diffusion image of the corresponding sample test point at each moment according to the prediction function.
For prediction functions
And setting i as an index value for generating a sample thermal diffusion image, wherein the image comprises pixel values of a plurality of sample test points, and the pixel values of the sample thermal diffusion image can be determined by the following function
Obtaining:
hair for sampleThe time starting points of the thermal process are different, the evolution of the heating process of the pixel points of the sample image can be simulated by the left and right movement of the value i in the interval, and the value i can be
And x is an arbitrary value, namely a dynamic change value of the heating time starting point of the analog pixel point.
For other prediction function models, the image dereferencing process is similar, and is not described herein again. By simulating different time sequences of hot spots at different positions of the sample test point in the periodic heating process through the sample image pixel points, the heating sequence of the sample at different positions in the periodic heating process can be visually reflected, so that the conduction process of the hot spots in the sample can be dynamically presented, and great convenience is brought to the analysis of subsequent data.
According to the technical scheme, the intensity information and the delay phase information of each pixel point on the image of the tested sample are obtained by using a phase-locked infrared camera or other imaging equipment related to the applied periodic excitation signal, and parameter values such as amplitude parameters, phase parameters and the like in a trigonometric function formula are determined according to the intensity information and the delay phase information. After the amplitude, the phase value and the like of each pixel point in the formula are determined, a series of images of which the intensity value of each pixel point changes along with time are obtained by properly sampling the formula.
The present embodiment further provides a time domain reconstruction method of the thermal diffusion process as shown in fig. 4, which includes, in addition to the above steps S100 to S104, further includes:
and step S105, synthesizing a thermal diffusion dynamic image of the sample based on the thermal diffusion image at the corresponding moment of the sample test point.
By superimposing the sample photograph on the sample thermal diffusion image generated in steps S100 to S104, the heating conditions of different positions (i.e., the test points and the corresponding sample pixel points) of the sample can be synthesized, and a dynamic video can be synthesized to show the change of the temperature distribution of the sample after the sample is periodically excited.
The following provides an example of practical application to a device under test based on the time domain reconstruction method of the thermal diffusion process of the present embodiment.
The object to be measured is a metal oxide semiconductor field effect transistor (MOS tube) with broken grid, and the MOS tube is fed with fixed frequency by constant current source
Square wave current (where square wave current is the excitation signal in the measurement).
Based on the test system shown in fig. 3, square wave signals are applied to the sample MOS transistor through the excitation signals (currents in fig. 3), and the amplitude a and the delay phase value of each pixel point on the image measured by the phase-locked infrared camera are obtained
And inputting the data into a computer to execute the time domain reconstruction method step flow of the embodiment.
The known phase-locked infrared camera has a circular frequency of
(
)，f c If the phase-locked frequency of the infrared camera is adopted, a function expression that the pixel value (namely, the infrared signal intensity value) of each pixel point of the MOS tube thermal diffusion image periodically changes along with time can be obtained by calculation is
。
Setting the number of image frames to be presented in a period as N, generating N infrared images by a computer, wherein the value of the pixel value of the same pixel in each image is
Wherein
Taking values to generate an index value for an image
. Fig. 5 illustrates a temperature distribution image at a time when T takes values of 0s,0.11s 0.21s,0.32s,0.43s,0.53s during one heating period (e.g., T =0.53 s), the x-axis and the y-axis in the image illustrating pixels (pixels), and the grayscale value illustrating the temperature unit kelvin (K).
In this example, N =6 is taken as an example, N may be any positive integer, and the larger N is, the slower the temperature distribution changes every two times during the heating process of the sample. Finally, the images are sequentially synthesized into a complete animation by the computer, and the whole process of heating the object to be measured (namely the MOS tube) can be restored through the animation.
The above example only considers the case where the first order term (fundamental frequency) of the infrared result is phase locked when the duty cycle of the periodic excitation signal is 50%. If a more refined reduction of the process in which the sample is heated is desired, many higher order terms are also taken into account. The prediction function of the change of the pixel value of the pixel point in the infrared image of the sample along with the time can be as follows:
wherein C is a constant, ensures
. The fourier transform theory proves that any periodic square wave function can be represented by the sum of a series of trigonometric functions. A can be determined in turn by single measurement with a phase-locked infrared camera 1 ，A 2 ，A 3 8230; (i.e. it is made of
) And
，
，
8230; (i.e. it is made of
) Equal parameters when the first
Pixel point value of sheet image is based on prediction function
As a function of i
：
The computer-implemented flowchart of the algorithm, as shown in fig. 6, is an algorithm for synthesizing an image of a sample in a period containing high-order terms, and includes:
at the beginning, namely: initializing i by the computer, wherein i =0;
capturing patterns, i.e. based on
Acquiring a sample image at initialization of i =0, after which i =1;
calculating out
And
i.e. by calculating A 1 ，A 2 ，A 3 …（
) And with
，
，
…（
) Generating a sample image of the current i value;
judging whether i reaches a preset order, namely checking whether the current i value is N;
if so, i = N, determining that i reaches a preset order, and synthesizing a video based on N images of the sample in the period;
if not, i = i +1, and the calculation continues under the current i value
And
and continuing to generate a sample image of the current i value.
Similarly, if the measured object is an IGBT, applying a periodic high-voltage excitation signal to both ends of the collector and emitter of the IGBT, applying a square-wave signal to the IGBT based on the test system shown in fig. 3 through the excitation signal, and obtaining the amplitude a and the delay phase value of each pixel point on the image measured by the phase-locked infrared camera
And inputting the data into a computer to execute the time domain reconstruction method step flow of the embodiment. The circular frequency of the known phase-locked infrared camera is
Then, a function expression of the periodic change of the pixel value (i.e. the infrared signal intensity value) of each pixel point of the thermal diffusion image of the IGBT tube with time can be calculated as
。
Setting the number of image frames to be presented in a period to be N, generating N infrared images by a computer, wherein the value of the pixel value of the same pixel in each image is N
Wherein
Taking values to generate an index value for an image
. Fig. 7 illustrates a temperature distribution image at a time T of 0s,0.53s,1.07s,1.6s,2.13s,2.67s during one heating period (e.g., T =2.67 s), the x-axis and the y-axis in the image illustrating pixels (pixels), and the grayscale value illustrating the temperature unit kelvin (K).
Similarly, if the measured object is a nitinol wire through which a periodic current flows, based on the test system shown in fig. 3, square signals are applied to the two ends of the sample wire through the excitation signal to obtain the amplitude a and the delay phase value of each pixel point on the image measured by the phase-locked infrared camera
And inputting the data into a computer to execute the time domain reconstruction method step flow of the embodiment. The known phase-locked infrared camera has a circular frequency of
Then the pixel value (namely red) of each pixel point of the metal wire thermal diffusion image can be calculatedExternal signal strength value) periodically varies with time as a function of the expression
。
Setting the number of image frames to be presented in a period to be N, generating N infrared images by a computer, wherein the value of the pixel value of the same pixel in each image is
Wherein
Taking values to generate index values for images
. Fig. 8 illustrates a temperature distribution image at a time T of 0s,0.11s,0.21s,0.32s,0.43s,0.53s in one heating period (e.g., T =0.53 s), the x-axis and the y-axis in the image illustrating pixels (pixels), and the grayscale value illustrating the temperature unit kelvin (K).
In particular, in the above examples of different samples, prediction functions were used
As a sampling function model, but based on the embodiment of the technical solution of the present invention, if a prediction function is adopted
、
Fourier expansion functions such as equal trigonometric functions or combinations of trigonometric functions are possible. And will not be described in detail herein.
Because the trigonometric function has periodicity, the computer is used for carrying out analog sampling on the sample test point based on the prediction function model to synthesize the sampleWhen pixel values of pixel points are calculated, based on a prediction function
、
、
When the functions further simulate the image, the generated functions
、
、
Etc. are all in the prediction function
The conversion is obtained, i is greater than or equal to 0 and less than or equal to N as a typical representation, in fact, because the trigonometric function has periodicity, i can be greater than or equal to 0-x and less than or equal to N-x, x is a deviation value of the heating time of the sample test point corresponding to the pixel point on the period, namely, the whole value interval of i can move left and right, the time starting points of the heating process of the sample are different, but the evolution process of the heating process of the sample along with the time is completely the same, and the description is omitted here.
The sample is an MOS tube, an IGBT tube, or a metal wire, but the sample of the present embodiment may be other electronic components, and the present embodiment is not illustrated.
By the technical scheme of the embodiment, the time sequence of hot spots at different positions can be simulated in the process that the sample is excited by an excitation signal and periodically heated, the intensity information and the delay phase information of the infrared signal of the test point are calculated and obtained based on the infrared signal measured by phase-locked infrared, the heating sequence of the sample at different positions in the periodic heating process can be visually reflected through the pixel value change of the dynamic image and the pixel point of the dynamic image, so that the conduction process of the hot spots in the sample can be dynamically presented, and great convenience is brought to the analysis of subsequent data.
The sequence of the heating points of the sample can represent the material properties of different areas on the electronic component and the depth information of the fault position in the component. According to the technical scheme, a sample thermal diffusion dynamic image is generated by a series of sample thermal diffusion images and sample images which are obtained by performing analog sampling synthesis on a prediction function based on periodic excitation signal frequency Fourier expansion, and the whole process of heating of different positions of a sample can be efficiently and simply obtained.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention.
Claims (12)
1. A method for time-domain reconstruction of a thermal diffusion process, comprising:
applying an excitation signal to a sample, wherein the excitation signal is a periodic signal, and the sample is excited by the excitation signal to emit an infrared signal at a sample test point;
collecting infrared signals of the sample test point, and calculating to obtain delay phase information based on the collected infrared signals;
performing Fourier expansion on the periodic function of the excitation signal to construct a prediction function of the infrared signal intensity value of the sample test point, wherein the prediction function comprises an amplitude parameter and a phase parameter;
determining an amplitude parameter and a phase parameter of the prediction function based on the periodic infrared signal of the sample test point and the delay phase information;
sampling the sample test point according to the prediction function to obtain a thermal diffusion image of the sample test point;
the sampling the sample test site according to the prediction function to obtain a thermal diffusion image of the sample test site comprises:
setting the thermal diffusion image time required to be acquired in the period based on the period of the excitation signal;
and generating a thermal diffusion image of the corresponding sample test point at each moment according to the prediction function.
2. The time-domain reconstruction method of claim 1, wherein the excitation signal is a square wave signal, and the fourier expanding the periodic function of the excitation signal comprises:
fourier expansion is performed on the periodic function of the excitation signal based on the waveform of the square wave signal.
3. The time-domain reconstruction method of claim 1 or 2, wherein said fourier expanding the periodic function of the excitation signal comprises:
expressing the periodic function based on a trigonometric function; alternatively, the first and second electrodes may be,
the periodic function is expressed based on a trigonometric function and a combination of trigonometric functions.
4. The time-domain reconstruction method of claim 1 or 2, wherein the prediction function is:
wherein t is a time, A is the amplitude parameter,
for the phase parameter, ω is the circular frequency, ω =2 π f c ，f c Is the phase locked frequency.
5. The time-domain reconstruction method of claim 1 or 2, wherein the prediction function is:
wherein t is time, C is constant, n is high-order term parameter corresponding to Fourier expansion, A n For the amplitude parameter corresponding to the higher order term parameter,
for the phase parameter corresponding to the higher order term, ω is the phase-locked frequency.
6. The time-domain reconstruction method of claim 1 or 2, wherein the prediction function is:
wherein t is time, C is constant, n is high-order term parameter corresponding to Fourier expansion, A max Is the maximum amplitude parameter of the excitation signal waveform, alpha is the duty cycle parameter of the excitation signal waveform, omega is the circular frequency, omega =2 pi f c ，f c Is the phase locked frequency.
7. The time-domain reconstruction method of claim 1, wherein said acquiring infrared signals and corresponding delay phase information of said sample test points comprises:
and carrying out single measurement on the sample test point through a phase-locked infrared camera, and determining the infrared signal and the corresponding delay phase information of the sample test point.
8. The time-domain reconstruction method of claim 1, wherein the sample site has a plurality of sample sites, the sampling the sample site according to the prediction function to obtain the thermal diffusion image of the sample site further comprising:
determining a sample sampling period according to the heating starting time of the sample test point;
setting the thermal diffusion image time required to be acquired in the period based on the sampling period;
and generating a thermal diffusion image of the corresponding sample test point at each moment according to the prediction function.
9. The time-domain reconstruction method of claim 1 or 8, further comprising:
and synthesizing a thermal diffusion dynamic image of the sample based on the thermal diffusion image at the corresponding moment of the sample test point.
10. The time domain reconstruction method of claim 1, wherein the sample is a MOS transistor, an IGBT transistor, or a wire.
11. A time domain reconstruction system for a thermal diffusion process, comprising:
the excitation unit is suitable for applying an excitation signal to a sample, the excitation signal is a periodic signal, and the sample is excited by the excitation signal to emit an infrared signal at a sample test point;
the acquisition unit is suitable for acquiring infrared signals of the sample test point;
the computing unit is suitable for computing delay phase information based on the collected infrared signals;
the construction unit is suitable for performing Fourier expansion on the periodic function of the excitation signal and constructing a prediction function of the infrared signal intensity value of the sample test point, wherein the prediction function comprises an amplitude parameter and a phase parameter;
the determining unit is suitable for determining the amplitude parameter and the phase parameter of the prediction function based on the infrared signal of the sample test point and the delay phase information;
the sampling unit is suitable for sampling the sample test point according to the prediction function so as to obtain a thermal diffusion image of the sample test point;
the sampling the sample test site according to the prediction function to obtain a thermal diffusion image of the sample test site comprises:
setting the thermal diffusion image time required to be acquired in the period based on the period of the excitation signal;
and generating a thermal diffusion image of the corresponding sample test point at each moment according to the prediction function.
12. The time-domain reconstruction system for thermal diffusion processes of claim 11, further comprising:
and the synthesis unit is suitable for synthesizing the thermal diffusion dynamic image of the sample based on the thermal diffusion image at the corresponding moment of the sample test point.
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