CN113589124B - Method and device for collecting variable frequency data - Google Patents
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Abstract
The invention relates to the frequency conversion data acquisition technology, and discloses a frequency conversion data acquisition method and device, wherein the setting of a sampling interval is that q/p=0.1 s; the acquisition interval is [0,100]]Segmenting a sampling interval; setting sampling points, wherein the number of sampling points of each section of the segmented sampling interval in the step S1 is m; the acquisition times are determined, and the acquisition times of the controller are j each time; when the controller collects, the collection is sequentially increased; acquisition array is determined according to sensitive parameter y q‑1 And the time t of acquisition q‑1 Obtaining an acquisition array A; and transforms the array a to obtain a new array. According to the invention, by utilizing the characteristic of thermal resistance test, the change is quick in a short time in the early period, the sampling rate is high, the slow sampling rate in the later period is low, and the sectional frequency conversion treatment is carried out on the whole transient cooling curve; the method not only meets the acquisition performance, ensures the accuracy of thermal resistance calculation, but also reduces the data volume, so that the software can run more smoothly and reliably.
Description
Technical Field
The invention relates to a variable frequency data acquisition technology and discloses a method and a device for acquiring variable frequency data of semiconductor device crusting thermal resistance.
Background
Semiconductor device crusting thermal resistance is one of the most important thermal parameters of a semiconductor device and is a parameter that measures the thermal diffusivity of the semiconductor device from the chip to the package surface.
The current international mainstream thermal resistance testing method is a transient double-interface method, namely, the surface of a device shell is contacted with an external ideal heat sink, the device is heated by high current, then the high current is rapidly cut off, meanwhile, a change curve of a Temperature Sensitive Parameter (TSP) along with time is collected, and finally, a thermal resistance curve is calculated, so that a transient cooling curve is obtained. Changing the contact thermal resistance between the device shell and the external ideal heat sink interface, collecting a transient cooling curve again according to the steps, and calculating a thermal resistance curve. The thermal resistance curves under the two different measurements are separated from the contribution points of the contact thermal resistance of the surface of the shell, so that the crusting thermal resistance value is obtained.
In the thermal resistance test process, temperature Sensitive Parameters (TSP) Vce need to be collected, vce represents the junction temperature of the device, the change curve of Vce is collected, namely, the thermal resistance change curve of the device is reflected, the thermal conduction paths of all levels in the device can be seen through the thermal resistance change curve, and the junction-to-shell junction thermal resistance value of the device can be accurately calculated, so that the thermal resistance test method has great value for the performance parameters of the device and the analysis of the damage in the device.
The Temperature Sensitive Parameter (TSP) must be acquired at the moment of heating and cutting off the high current, the sampling speed must be fast enough to ensure the accuracy of the calculated crusting thermal resistance, the sampling rate is generally required to be 1M/S, the time granularity reaches 1us, but the whole transient cooling curve acquisition requires about 100S, the high-speed sampling rate and the time length of about 100S can lead to acquisition of mass data, great performance consumption is caused for data storage and data operation, and the user experience of using software is extremely influenced.
The data collected by the device at the moment of external heating and heavy current removal contains richer device information, and the device needs more than 100s in the whole cooling link, so that the data in a short time in the early stage needs to be collected at a high speed and the trouble of large data quantity is faced.
The data acquisition in the prior art cannot well meet the performance and greatly reduce the data volume.
For example, the patent name: a pulse power alternating current aging test platform and a test method are provided, wherein the patent application number is: CN201910778881.2; the application date is as follows: 2019-08-22, the patent application discloses a pulse power alternating current aging test platform and a test method, which are used for testing the aging condition of an IGBT module under the pulse power condition and evaluating the service life of the IGBT module, wherein the test platform comprises the following units: a main circuit unit for simulating a complete burn-in cycle; the measuring circuit unit is used for measuring the saturation voltage drop and the crusting thermal resistance of the IGBT module; the protection circuit unit is used for correspondingly protecting the test platform; the information processing unit is used for controlling and processing various information in the test process; and the upper computer is used for receiving various information sent by the information processing unit and displaying and controlling the information.
The method for data acquisition in the prior art can not well meet the performance and greatly reduce the data quantity at the same time.
Disclosure of Invention
Aiming at the defects that the data acquisition can not well meet the performance and the data quantity is greatly reduced at the same time, the invention provides a method and a device for acquiring variable frequency data.
In order to solve the technical problems, the invention is solved by the following technical scheme:
a method for collecting variable frequency data comprises the steps of,
s1, setting a sampling interval, wherein the controller samples frequency p samples/S according to a temperature sensitive parameter Vce, and the number q samples/time of each time; and q/p=0.1 s; the acquisition interval is [0,100], and the sampling interval is segmented;
s2, setting sampling points, wherein the number of sampling points of each section of the segmented sampling interval in S1 is m;
s3, determining the acquisition times, wherein the acquisition times of the controller are j each time; when the controller collects, the collection is sequentially increased, and j=100×q/p;
s4, determining an acquisition array according to the sensitive parameter y q-1 And the time t of acquisition q-1 Obtaining an acquisition array A; wherein A= [ (y) 0 , t 0 )、( y 1 , t 1 )、( y 2 , t 2 )、…、( y q-1 , t q-1 )]The method comprises the steps of carrying out a first treatment on the surface of the And transforms the array a to obtain a new array.
Preferably, the segments are divided into 8 segments on the logarithmic axis in time intervals of [0,10 respectively -5 ]、[10 -5 ,10 -4 ]、[10 -4 ,10 -3 ]、[10 -3 ,10 -2 ]、[10 -2 ,10 -1 ]、[10 -1 ,10 0 ]、[10 0 ,10 1 ]、[10 1 ,10 2 ]。
Preferably, when j=1, the transformation of array a comprises the steps of,
first step, time period [0, q/p]The first 5 segments of the 8-segment time period contained in S1, i.e., [0,10 ] -5 ]、[10 -5 ,10 -4 ]、[10 -4 ,10 -3 ]、[10 -3 ,10 -2 ]、[10 -2 ,10 -1 ]After being cut according to the time period, the array A becomes A1, A2, A3, A4 and A5;
secondly, calculating a parameter i= (upper time limit-lower time limit) p/m in each period of time;
thirdly, judging the size of i, wherein when i is less than 1, the data points in the period of time do not need to be transformed;
judging the size of i, and when i is more than 1, converting the data points in the period of time into 1 point according to the aggregation of i points, so that the data points in the period of time are downsampled to the required points;
fifth, the record group B [ (t 0, y 0), …, (tn, yn) ] is a data point set after conversion of the group A, the converted or non-converted data subsets A1, A2, A3, A4, A5 in each time period in the above steps are converted into B1, B2, B3, B4, B5, and the new group B is combined in time sequence.
Preferably, 1<When j is less than or equal to 10, the instant time period [10 -1 ,10 0 ]；
The calculation parameter i= (upper time limit-lower time limit) p/m=0.9 p/m,
when j increases each time, q data points acquired each time are aggregated according to i points to obtain a new array, and the arrays after each transformation in the period of time are combined into a new array C [ (t 0, y 0), …, (tn, yn) ] of the period.
Preferably, when 10<When j is less than or equal to 100, the instant time period [10 0 ,10 1 ]；
Calculating a parameter i= (upper time limit-lower time limit) ×p/m= 9*p/m;
when j increases each time, q data points acquired each time are aggregated according to i points to obtain a new array, and the arrays after each transformation in the period of time are combined into a new array D [ (t 0, y 0), …, (tn, yn) ] of the period.
Preferably, when j>100 hours, i.e. time period [10 1 ,10 2 ]When j increases each time, q data points acquired each time are aggregated into one point according to q points,
the calculation parameter i= (upper time limit-lower time limit) ×10/m=900/m, and when j increases i times, the points of the previous i times are aggregated into a point, and the aggregated points are combined into the new segment array E.
Preferably, the array B, C, D, E is also combined into complete sample data in time sequence.
Preferably, the method of polymerization includes an average method or a median method.
A device for collecting frequency conversion data is realized by a frequency conversion data collecting method.
The invention has the remarkable technical effects due to the adoption of the technical scheme: the method solves the problem of huge data volume by carrying out sectional variable frequency processing on the whole transient cooling curve acquisition, and ensures that enough points are ensured in the initial extremely short time.
Drawings
Fig. 1 is a flow chart of the present invention.
FIG. 2 is a schematic diagram of transient cooling curves for non-variable frequency data acquisition of the present invention.
Fig. 3 is a schematic diagram of transient cooling curves for variable frequency data acquisition of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Example 1
A method for collecting variable frequency data comprises the steps of,
s1, setting a sampling interval, wherein the controller samples frequency p samples/S according to a temperature sensitive parameter Vce, and the number q samples/time of each time; and q/p=0.1 s; the acquisition interval is [0,100], and the sampling interval is segmented;
s2, setting sampling points, wherein the number of sampling points of each section of the segmented sampling interval in S1 is m;
s3, determining the acquisition times, wherein the acquisition times of the controller are j each time; when the controller collects, the collection is sequentially increased, and j=100×q/p;
s4, determining an acquisition array according to the sensitive parameter y q-1 And the time t of acquisition q-1 Obtaining an acquisition array A; wherein A= [ (y) 0 , t 0 )、( y 1 , t 1 )、( y 2 , t 2 )、…、( y q-1 , t q-1 )]The method comprises the steps of carrying out a first treatment on the surface of the And transforms the array a to obtain a new array.
The method can meet the requirements of acquisition performance, ensure the accuracy of thermal resistance calculation and greatly reduce the data volume so that software can run more smoothly and reliably in the transient cooling curve acquisition process.
The segmentation is divided into 8 segments on logarithmic coordinate axis in time interval of [0,10 ] -5 ]、[10 -5 ,10 -4 ]、[10 -4 ,10 -3 ]、[10 -3 ,10 -2 ]、[10 -2 ,10 -1 ]、[10 -1 ,10 0 ]、[10 0 ,10 1 ]、[10 1 ,10 2 ]。
When j=1, the transformation of array a includes the steps of,
first step, time period [0, q/p]The first 5 segments of the 8-segment time period contained in S1, i.e., [0,10 ] -5 ]、[10 -5 ,10 -4 ]、[10 -4 ,10 -3 ]、[10 -3 ,10 -2 ]、[10 -2 ,10 -1 ]After being cut according to the time period, the array A becomes A1, A2, A3, A4 and A5;
secondly, calculating a parameter i= (upper time limit-lower time limit) p/m in each period of time;
thirdly, judging the size of i, wherein when i is less than 1, the data points in the period of time do not need to be transformed;
judging the size of i, and when i is more than 1, converting the data points in the period of time into 1 point according to the aggregation of i points, so that the data points in the period of time are downsampled to the required points;
fifth, the record group B [ (t 0, y 0), …, (tn, yn) ] is a data point set after conversion of the group A, the converted or non-converted data subsets A1, A2, A3, A4, A5 in each time period in the above steps are converted into B1, B2, B3, B4, B5, and the new group B is combined in time sequence.
1<When j is less than or equal to 10, the instant time period [10 -1 ,10 0 ]；
The calculation parameter i= (upper time limit-lower time limit) p/m=0.9 p/m,
when j increases each time, q data points acquired each time are aggregated according to i points to obtain a new array, and the arrays after each transformation in the period of time are combined into a new array C [ (t 0, y 0), …, (tn, yn) ] of the period.
When 10<When j is less than or equal to 100, the instant time period [10 0 ,10 1 ]；
Calculating a parameter i= (upper time limit-lower time limit) ×p/m= 9*p/m;
when j increases each time, q data points acquired each time are aggregated according to i points to obtain a new array, and the arrays after each transformation in the period of time are combined into a new array D [ (t 0, y 0), …, (tn, yn) ] of the period.
When j is>100 hours, i.e. time period [10 1 ,10 2 ]When j increases each time, q data points acquired each time are aggregated into one point according to q points,
the calculation parameter i= (upper time limit-lower time limit) ×10/m=900/m, and when j increases i times, the points of the previous i times are aggregated into a point, and the aggregated points are combined into the new segment array E.
Also included is the time sequential assembly of the arrays B, C, D, E into complete sample data.
The polymerization method includes an average method or a median method.
Example 2
Based on the embodiment 1, a device for collecting frequency conversion data is realized by a frequency conversion data collecting method.
Example 3
On the basis of example 1, pair y during polymerization n Averaging, corresponding t n It is also necessary to average and to compose new points (t n 、y n ) Thus in time period [10 ] -4 ,10 -3 ]The inner groups are aggregated into 1 point according to i=9 points, so that the array A3 contains 100 points, time period [10 ] -3 ,10 -2 ]The inner groups are aggregated into 1 point according to i=90 points, so that the array A4 contains 100 points, time period [10 ] -2 ,10 -1 ]The i=900 points are aggregated into 1 point, so that the array A5 contains 100 points, and thus the data points in the 3 periods are downsampled to the required points.
The array B [ (t 0, y 0), …, (tn, yn) ] is a data point set transformed by the array A, the transformed or non-transformed data subsets A1, A2, A3, A4, A5 of each time period are transformed into B1, B2, B3, B4, B5, the new array B [ (t 0, y 0), …, (tn, yn) ] is combined in time sequence, and the value B comprises 400 points;
when 1<When j is less than or equal to 10, the instant time period [10 -1 ,10 0 ]Calculating parameter i= (upper time limit-lower time limit) p/m=0.9 p/m=9000, when j increases each time, aggregating q=100deg.K data points acquired each time according to i=9000 points to obtain a new array, and combining the arrays after each transformation in the period into a new array C [ (t 0, y 0), …, (tn, yn) of the period]Array C contains 99 points;
when 10<When j is less than or equal to 100, the instant time period [10 0 ,10 1 ]Calculating parameter i= (upper time limit-lower time limit) ×p/m= 9*p/m=90000, when j increases each time, aggregating q data points acquired each time according to i=90000 points to obtain a point, and combining the arrays after each transformation in the period of time into a new array D [ (t 0, y 0), …, (tn, yn) of the period]Array D contains 90 points;
when j is>100 hours, i.e. time period [10 1 ，10 2 ]When j increases each time, q data points acquired each time are aggregated into one point according to q=100deg.C points, and then a parameter i= (upper time limit-lower time limit) 10 × is calculatedm=900/m= 9,j, 9 times of increasing, the previous 9 times of points are aggregated into one point, and the aggregated points are combined into a new segment array E [ (t 0, y 0), …, (tn, yn)]Array E contains 100 points;
when the acquisition is finished, the arrays B, C, D, E are combined into complete sampling data according to the time sequence, the number of the arrays is 689, and the data size is small.
The data volume collected in the time periods [0,100] is greatly reduced, but the data in the us level is not lost in the actual period of much earlier period, the data in the short period is reserved, the frequency conversion data collection in the whole period is achieved, fig. 2 is the data collection result of the original frequency conversion algorithm not adopted, 100s data is collected according to the sampling rate p=1M S/s, the whole data volume is 100M data points, a large amount of resources are consumed for curve display, storage and calculation, the curve collected after the frequency conversion algorithm is adopted in the position of fig. 3, the data of 689 data points in the whole curve is not lost in the short period of earlier period, and the curve trend is the same as that of the original curve.
Claims (4)
1. A method for collecting variable frequency data comprises the steps of,
s1, setting a sampling interval, wherein the controller samples frequency p samples/S according to a temperature sensitive parameter Vce, and the number q samples/time of each time; and q/p=0.1 s; the acquisition interval is [0,100], and the sampling interval is segmented;
s2, setting sampling points, wherein the number of sampling points of each section of the segmented sampling interval in S1 is m;
s3, determining the acquisition times, wherein the acquisition times of the controller are j each time; when the controller collects, the collection is sequentially increased, and j=100×q/p;
s4, determining an acquisition array according to the sensitive parameter y q-1 And the time t of acquisition q-1 Obtaining an acquisition array A; wherein A= [ (y) 0 , t 0 )、( y 1 , t 1 )、( y 2 , t 2 )、…、( y q-1 , t q-1 )]The method comprises the steps of carrying out a first treatment on the surface of the Transforming the array A to obtain a new array;
the segments being time-of-dayThe interval is divided into 8 sections on the logarithmic coordinate axis, which are respectively [0,10 ] -5 ]、[10 -5 ,10 -4 ]、[10 -4 ,10 -3 ]、[10 -3 ,10 -2 ]、[10 -2 ,10 -1 ]、[10 -1 ,10 0 ]、[10 0 ,10 1 ]、[10 1 ,10 2 ]；
When j=1, the transformation of array a includes the steps of,
first step, time period [0, q/p]The first 5 segments of the 8-segment time period contained in S1, i.e., [0,10 ] -5 ]、[10 -5 ,10 -4 ]、[10 -4 ,10 -3 ]、[10 -3 ,10 -2 ]、[10 -2 ,10 -1 ]After being cut according to the time period, the array A becomes A1, A2, A3, A4 and A5;
secondly, calculating a parameter i= (upper time limit-lower time limit) p/m in each period of time;
thirdly, judging the size of i, wherein when i is less than 1, the data points in the period of time do not need to be transformed;
judging the size of i, and when i is more than 1, converting the data points in the period of time into 1 point according to the aggregation of i points, so that the data points in the period of time are downsampled to the required points;
fifthly, recording the data sets B [ (t 0, y 0), …, (tn, yn) ] as data point sets after the conversion of the data set A, converting the converted or non-converted data subsets A1, A2, A3, 4, A5 of each time period into B1, B2, B3, B4, B5 in the above steps, and combining the data sets into a new data set B according to the time sequence;
1<when j is less than or equal to 10, the instant time period [10 -1 ,10 0 ]；
The calculation parameter i= (upper time limit-lower time limit) p/m=0.9 p/m,
when j increases each time, q data points acquired each time are aggregated according to i points to obtain a new array, and the arrays after each transformation in the period of time are combined into a new array C [ (t 0, y 0), …, (tn, yn) ] in the period of time;
when 10<When j is less than or equal to 100, i.eTime period [10 ] 0 ,10 1 ]；
Calculating a parameter i= (upper time limit-lower time limit) ×p/m= 9*p/m;
when j increases each time, q data points acquired each time are aggregated according to i points to obtain a new array, and the arrays after each transformation in the period of time are combined into a new array D [ (t 0, y 0), …, (tn, yn) ] in the period of time;
when j is>100 hours, i.e. time period [10 1 ,10 2 ]When j increases each time, q data points acquired each time are aggregated into one point according to q points,
the calculation parameter i= (upper time limit-lower time limit) ×10/m=900/m, and when j increases i times, the points of the previous i times are aggregated into a point, and the aggregated points are combined into the new segment array E.
2. A method of variable frequency data acquisition as claimed in claim 1, further comprising combining the arrays B, C, D, E into complete sampled data in a time sequence.
3. A method of variable frequency data acquisition according to any one of claims 1-2, wherein the method of aggregation comprises an averaging method or a median method.
4. A device for variable frequency data acquisition, characterized by comprising a device realized by the method of any one of claims 1-3.
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