CN111553611B - Method and device for obtaining design yield - Google Patents
Method and device for obtaining design yield Download PDFInfo
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- CN111553611B CN111553611B CN202010381928.4A CN202010381928A CN111553611B CN 111553611 B CN111553611 B CN 111553611B CN 202010381928 A CN202010381928 A CN 202010381928A CN 111553611 B CN111553611 B CN 111553611B
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Abstract
The invention discloses a method and a device for obtaining design yield, which are characterized in that a first type of input parameter space which possibly does not accord with a performance index is rapidly positioned through amplifying standard deviation, a first type of function is obtained based on sampling points corresponding to the first type of input parameter space and corresponding performance measurement values, a second type of sampling points are obtained by random sampling based on standard deviation of original distribution of each input random parameter of a design object, a performance estimation value is obtained by utilizing the first type of function for the second type of sampling points which fall in the first type of input parameter space, a third type of sampling points which do not accord with the performance index is determined, the design yield of the design object which accords with the performance index in the corresponding whole random parameter space is calculated according to the number of the third type of sampling points and the number of the second type of sampling points, and the efficiency of obtaining the design yield which accords with the performance index is improved.
Description
Technical Field
The invention relates to the technical field of computer simulation, in particular to a method and a device for obtaining design yield.
Background
For a design object with a high dimension of input random parameter space and input random parameter distribution tending to be flattened, for example, chip design is carried out under the support of a modern integrated circuit manufacturing process, since the design object pursues a very high yield, such as 99.999999%, a high sigma Monte Carlo analysis (Monte Carlo Method) is required to carry out the yield of the design object, or referred to as design yield.
Conventional low sigma monte carlo analysis methods face technical challenges in terms of speed/time and accuracy when used in high sigma monte carlo analysis, resulting in extremely low analysis efficiency of design yield.
Disclosure of Invention
In view of the above, the invention discloses a method and a device for obtaining a design yield, which can improve the efficiency of obtaining the design yield conforming to performance indexes and accelerate the speed of Monte Carlo analysis.
In order to achieve the above purpose, the technical scheme disclosed by the invention is as follows:
in a first aspect, the present invention discloses a method for obtaining a design yield, the method comprising:
amplifying standard deviation of each input random parameter of the design object, and randomly sampling each input random parameter based on the amplified standard deviation to obtain a plurality of first-class sampling points;
acquiring performance measured values of all the first type sampling points;
determining a first type of input parameter space which may not meet design indexes based on the performance measurement values of the first type of sampling points and the corresponding performance index values;
obtaining a first class function representing the corresponding relation between the parameter values possibly not conforming to the design index and the performance measured values based on the sampling points corresponding to the first class input parameter space corresponding to each input random parameter and the corresponding performance measured values;
Based on standard deviation of original distribution of each input random parameter of a design object, carrying out random sampling to obtain second class sampling points, calculating the second class sampling points falling in the first class input parameter space by using the first class function to obtain a performance estimation value, and determining a third class sampling point which does not accord with a design index according to the performance estimation value;
and calculating the design yield of the design object conforming to the design index in the corresponding overall random parameter space according to the number of the third class sampling points and the number of the second class sampling points.
Optionally, the determining, based on the performance measurement values and the corresponding performance index values of the first class sampling points, a first class input parameter space that may not meet the design index includes:
selecting a sampling point set of which the performance measured value is in a preset range from the first type of sampling points, wherein the preset range is obtained according to the performance index value corresponding to the input random parameter;
inputting random parameters to any one of the design objects, wherein the value range of the random parameters is corresponding to all sampling points in the selected sampling point set;
and determining the value range corresponding to all the input random parameters in the design object as the first type of input parameter space.
Optionally, the obtaining a first class of functions that characterizes the correspondence between the parameter values that may not meet the design index and the performance measurement values based on the sampling points and the corresponding performance measurement values corresponding to the first class of input parameter spaces corresponding to the random input parameters includes:
subdividing the first type input parameter space corresponding to each input random parameter to obtain a plurality of subdivided first type input parameter spaces;
and extracting functions of sampling points corresponding to the subdivided first-class input parameter spaces and corresponding performance measured values to obtain first-class functions representing the corresponding relation between the parameter values possibly not conforming to the design index and the performance measured values.
Optionally, before amplifying the standard deviation of each input random parameter of the design object, and randomly sampling each input random parameter based on the amplified standard deviation to obtain a plurality of first-type sampling points, the method further includes:
dividing the whole random parameter space corresponding to the design object to obtain a plurality of subdivision spaces so as to obtain the second type sampling point and the third type sampling point corresponding to each subdivision space;
the calculating the design yield in the input random parameter space according to the number of the third class sampling points and the number of the second class sampling points comprises the following steps:
And calculating to obtain the design yield corresponding to the whole random parameter space based on the number of the second type sampling points corresponding to each subdivision space, the number of the third type sampling points and the ratio of each subdivision space to the whole random parameter space.
Optionally, the dividing the random parameter space to obtain each subdivision space includes:
and dividing the random parameter space by taking the parameter intermediate value point as a center, respectively taking the boundary direction up and down and according to the sequence from large space to small space, so as to obtain a plurality of subdivision spaces.
Optionally, amplifying standard deviation of each input random parameter of the design object, and randomly sampling each input random parameter based on the amplified standard deviation to obtain a plurality of first-type sampling points, including:
amplifying standard deviations of the random parameters of each input of the design object by utilizing a plurality of different amplification factors respectively to obtain a plurality of amplified standard deviations;
and randomly sampling each input random parameter based on the standard deviation after amplification to obtain a plurality of first-type sampling points.
Optionally, the process of determining the overall random parameter space corresponding to the design object is as follows:
Acquiring the sensitivity of each input random parameter corresponding to the design object, and selecting the input random parameters with the sensitivity greater than or equal to a preset threshold value;
and determining the selected regional range of the input random parameters as an integral random parameter space corresponding to the design object.
Optionally, the randomly sampling to obtain a second type of sampling point based on standard deviation of original distribution of each input random parameter of the design object includes:
randomly sampling any input random parameter of the design object, and judging whether the value of the input random parameter is in a first type of input parameter space corresponding to the input random parameter;
if the value of the input random parameter is in the corresponding first type of input parameter space, continuing to randomly sample the next input random parameter;
if the value of the input random parameter is not in the corresponding first type of input parameter space, marking the current sampling point outside the first type of input parameter space;
if all the values of the input random parameters of the design object are in the corresponding first type of input parameter space, marking the current sampling point in the first type of input parameter space;
Wherein all sampling points marked in the first type of input parameter space and all sampling points marked outside the first type of output parameter space constitute the second type of sampling points.
In a second aspect, the present invention discloses a device for obtaining a design yield, the device comprising:
the amplifying unit is used for amplifying standard deviation of each input random parameter of the design object, and randomly sampling each input random parameter based on the amplified standard deviation to obtain a plurality of first-class sampling points;
the first acquisition unit is used for acquiring the performance measured value of each first type of sampling point;
a determining unit, configured to determine a first type of input parameter space that may not conform to a design index, based on the performance measurement values and the corresponding performance index values of the first type of sampling points;
the second acquisition unit is used for acquiring a first class function representing the corresponding relation between the parameter value possibly not conforming to the design index and the performance measured value based on the sampling point corresponding to the first class input parameter space corresponding to each input random parameter and the corresponding performance measured value;
the sampling determining unit is used for carrying out random sampling to obtain second class sampling points based on standard deviation of original distribution of each input random parameter of the design object, calculating the second class sampling points falling in the first class input parameter space by using the first class function to obtain a performance estimated value, and determining a third class sampling point which does not accord with the performance index according to the performance estimated value;
And the calculating unit is used for calculating the design yield in the input random parameter space according to the number of the third type of sampling points and the number of the second type of sampling points.
Optionally, the second obtaining unit includes:
the subdivision module is used for subdividing the first type of input parameter space corresponding to each input random parameter to obtain a plurality of subdivided first type of input parameter spaces;
and the function extraction module is used for carrying out function extraction on sampling points corresponding to the plurality of subdivided first-class input parameter spaces and corresponding performance measured values to obtain first-class functions representing the corresponding relation between parameter values possibly not conforming to design indexes and the performance measured values.
According to the scheme, a first type of input parameter space which possibly does not accord with the performance index is rapidly located through amplification standard deviation, a first type of function is obtained based on sampling points corresponding to the first type of input parameter space and corresponding performance measurement values, random sampling is carried out based on standard deviation of original distribution of each input random parameter of a design object to obtain a second type of sampling points, the second type of sampling points falling in the first type of input parameter space are utilized to obtain a performance estimation value, a third type of sampling points which do not accord with the performance index are determined, the design yield of the design object meeting the performance index in the corresponding overall random parameter space is calculated according to the number of the third type of sampling points and the number of the second type of sampling points, and efficiency of obtaining the design yield meeting the performance index is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a method for obtaining a design yield according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of a first class of functions disclosed in an embodiment of the present application for obtaining a corresponding value of a parameter and a performance measurement in each subdivision space in a first class of input parameter spaces;
FIG. 3 is a block diagram of a Monte Carlo analysis result in an entire input parameter space calculated according to the size of each subdivision space and the Monte Carlo analysis result, which is disclosed in the embodiment of the present application;
FIG. 4 is a schematic diagram of an input random parameter space division disclosed in an embodiment of the present application;
FIG. 5 is a schematic diagram of another input random parameter space division disclosed in an embodiment of the present application;
fig. 6 is a schematic structural diagram of a device for obtaining a design yield according to an embodiment of the present disclosure;
Fig. 7 is a schematic structural diagram of another apparatus for obtaining a design yield according to an embodiment of the present disclosure.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flow chart of a method for obtaining a design yield according to an embodiment of the present disclosure is shown, and as shown in fig. 1, the method mainly includes the following steps:
s110: and amplifying standard deviation of each input random parameter of the design object, and randomly sampling each input random parameter based on the amplified standard deviation to obtain a plurality of first-class sampling points.
The standard deviation of each input random parameter of the design object is amplified by the following steps:
by multiplying the standard deviation of the individual input random parameters of the design object by the magnification factor. Wherein the amplification factor may be a real number greater than 1. The preferred amplification factor of this scheme is not less than 3.
The purpose of amplifying the standard deviation of each input random parameter of the design object is to change the distribution probability of each input random parameter, so that the sampling of the minimum probability can be remarkably improved, and the accuracy and the speed of Monte Carlo analysis of high sigma are improved.
The standard deviation of each input random parameter of the design object is amplified by utilizing a plurality of different amplification factors respectively to obtain a plurality of amplified standard deviations, and each input random parameter is randomly sampled based on each amplified standard deviation to obtain a plurality of first-class sampling points. Then, the processes of S120 to S160 are performed for the sets of the plurality of first-type sampling points, respectively.
S120: and obtaining performance measured values of each first type of sampling point.
And obtaining corresponding performance measured values by performing computer simulation on each first type of sampling point.
Wherein the computer simulation is to execute a simulation program on a computer to obtain a performance measurement value characterizing the design object.
S130: based on the performance measurements of the first class of sampling points and the corresponding performance index values, a first class of input parameter spaces that may not meet the design index are determined.
The parameter space is a value space formed by the value ranges of the random parameters; the first type of input parameter space refers to a value space in which the value of each input random parameter does not accord with the design index.
Firstly, selecting a sampling point set with a performance measured value within a preset range from first-class sampling points, then, inputting random parameters into any one of design objects, selecting the value range of the input random parameters corresponding to all sampling points in the sampling point set, and finally, determining the value range corresponding to all input random parameters in the design objects as a first-class input parameter space.
The preset range is obtained according to the performance index value of each input random parameter.
Further, the process of specifically determining the first type of input parameter space that may not meet the design criteria is as follows:
let the measured value of the property be V mi The performance index value is V spec ，V mi ≤V spec Is a sample meeting design criteria, V mi ＞V spec * Beta extractionThe samples are samples of a first type of sampling point.
For any input random parameter P i According to the selected n sampling points S i，j Obtaining the random parameter P i Discrete value V of (2) i，j . Further, the input random parameter P in the selected sample set i Obtain the value range V from each discrete value of (a) i，min ,V i，max ]. Value range V of all input random parameters in design object under selected sampling set i，min ,V i，max ]Together, a first type of input parameter space is constructed that may not meet design criteria.
Where β is a positive number less than 1, and for high sigma analysis, β typically takes a value of 0.8.
j=1, …, n. n is the number of samples selected.
Upper limit point V i，min ＝min(V i，1 ，V i，2 ，…，V i，n ) Lower limit point V i，max ＝max(V i，1 ，V i，2 ，…，V i，n )。
It should be noted that, in practical application, the first type of input parameter space that may not meet the design index may be determined together according to the value range of each input random parameter under the selected sampling set and the theoretical value range of the input random parameter.
If the value range of the input random parameter under the selected sample is consistent with the theoretical value range of the input random parameter, the value range of the input random parameter under the selected sample can be ignored when the first type of input parameter space which possibly does not accord with the design index is determined, namely the theoretical value range of the input random parameter is obtained.
If the upper limit or the lower limit of the value range of the input random parameter under the selected sample is close to the upper limit or the lower limit of the theoretical value range of the input random parameter, and the value of the input random parameter between the upper limit of the value range under the selected sample and the upper limit of the theoretical value of the input random parameter is obtained, the value of the input random parameter between the lower limit of the value range under the selected sample and the lower limit of the theoretical value range of the input random parameter is obtained, so that the performance measured value does not accord with the performance index value, and the upper limit or the lower limit of the theoretical value range of the input random parameter is used as the upper limit or the lower limit of the value range of the input random parameter under the selected sample, thereby avoiding missing the sample in the minimum probability value area.
S140: based on the sampling points and the corresponding performance measured values corresponding to the first type of input parameter space corresponding to each input random parameter, a first type of function representing the corresponding relation between the parameter values possibly not conforming to the design index and the performance measured values is obtained.
The first class input parameter space and the sampling point number determined in the previous step are smaller, so that the method is beneficial to extracting the high-precision first class function from the function extraction perspective, and the efficiency of extracting the first class function is improved.
In one embodiment of the present application, to further improve the accuracy of the monte carlo analysis, as shown in fig. 2, the samples in the first type of input parameter space that may not meet the design criteria may be further divided, so as to further subdivide the first type of input parameter space that may not meet the design criteria into a plurality of smaller first type of input parameter spaces that may not meet the design criteria. Then, performing function extraction on the sampling points corresponding to each subdivided first type of input parameter space to obtain a first type of function corresponding to the parameter values and the performance measured values in each subdivided space in the first type of input parameter space, for example, f in FIG. 2 1 (P 1 ，P 2 ，…，P n )、f 2 (P 1 ，P 2 ，…，P n )、f 3 (P 1 ，P 2 ，…，P n ). The first type of input parameter space is subdivided, and the function is extracted aiming at the subdivided input parameter space, so that the accuracy of the function is improved, and the efficiency of the function is improved. In one embodiment of the present application, a clustering algorithm may be employed to divide the sampling points of the first type of input parameter space, for example, a K-means clustering algorithm (K-means clustering algorithm).
S150: based on standard deviation of original distribution of each input random parameter of the design object, carrying out random sampling to obtain second class sampling points, calculating the second class sampling points falling in the first class input parameter space by using a first class function to obtain a performance estimation value, and determining a third class sampling point which does not accord with the design index according to the performance estimation value.
In one embodiment of the present application, when the second type of sampling point is obtained by performing random sampling on the random parameter space, the parameter space outside the first type of input parameter space is incompletely sampled to increase the sampling speed.
Specifically, the process of obtaining the second type of sampling point is as follows: in the process of obtaining a complete sampling point, randomly sampling any input random parameter of a design object, and judging whether the value of the input random parameter is in a first type of input parameter space corresponding to the input random parameter; if the value of the input random parameter is in the corresponding first type of input parameter space, continuing to randomly sample the next input random parameter; if the value of the input random parameter is not in the corresponding first type of input parameter space, marking the current sampling point outside the first type of input parameter space; if all the values of the input random parameters of the design object are in the corresponding first type of input parameter space, marking the current sampling point in the first type of input parameter space.
Wherein all sampling points marked in the first type input parameter space and all sampling points marked outside the first type output parameter space constitute the second type sampling points.
The second type of sampling point is a set of all sampling points corresponding to the design object.
Each sampling point corresponds to a design object, and each sampling point comprises a plurality of input random parameters.
If the sampled value of a certain input random parameter falls to V i，min ,V i，max ]The method further comprises the steps of calculating a performance estimated value of the sampling point by using the extracted first class function, and determining whether the sampling point does not accord with the design index according to the performance estimated value. If the sampling value of the input random parameterNot falling at [ V i，min ,V i，max ]Indicating that the sampling point meets the design criteria.
Judging whether the sampling point corresponding to the design object is in the first type input parameter space or not, if the sampling point corresponding to the design object is not in the first type input parameter space, ending the construction of the current sampling point, and not needing to sample other input parameters in the current sampling, thereby saving the sampling time and improving the sampling efficiency.
Further, in the application scenario in which the first type input parameter space is subdivided in S140 and then the function is extracted, referring to fig. 2, the performance estimated value of the sampling point is calculated by using the first type function corresponding to the subdivided first type input parameter space for the class sampling point that falls in the subdivided first type input parameter space, so that the performance estimated value of the second type sampling point is more accurately obtained.
S160: and calculating the design yield of the design object conforming to the design index in the corresponding overall random parameter space according to the number of the third class sampling points and the number of the second class sampling points.
And calculating the design yield corresponding to the overall random parameter space based on the number of the second type sampling points, the number of the third type sampling points and the duty ratio of each subdivision space to the overall random parameter space.
According to the method for obtaining the design yield, the standard deviation of the distribution of the input random parameters is amplified, so that the distribution probability of the input random parameters is changed, the sampling probability of sampling points obtained by sampling with small probability is increased, and therefore the accuracy and the speed of high sigma Monte Carlo analysis are improved. In addition, the number of the first type of input parameter spaces and sampling points which are determined in the scheme and possibly not in accordance with the design index is smaller, so that the extraction time for extracting the function from the sampling points corresponding to the first type of input parameter spaces is shorter, the function with high precision is extracted, and the calculation efficiency and the precision of the design yield are improved finally.
Referring to fig. 3, it is shown that the input random parameter space is divided to obtain each subdivision space, and then the processes of S110 to S150 are executed in each subdivision space in parallel, and the monte carlo analysis result in the whole input parameter space is calculated according to the size of each subdivision space and the monte carlo analysis result, that is, the yield of the whole input parameter space accords with the design index.
Wherein, K1, K2, K3 and K4 are subspaces after dividing the input random parameter space. After the steps S110 to S150 in the foregoing embodiments are executed on each of the subspaces K1, K2, K3 and K4, the third type of sampling point numbers corresponding to each of the subspaces K1, K2, K3 and K4 are obtained.
And then, calculating the design yield of each subspace according to the number of the third type sampling points and the number of the second type sampling points corresponding to each subspace K1, K2, K3 and K4. Specifically, assuming that the size of the entire random parameter space is V, the size of each subdivision space is V 1 、V 2 、…、V n The sampling point of each subdivision space (i.e. the second type of sampling point) is S 1 、S 2 、…、S n The sampling point (i.e. the third type of sampling point) of each subdivision space which does not meet the design index is F 1 、F 2 、…、F n 。
The yield rate of the whole input parameter space meeting the design index is as follows
In one embodiment of the invention, the process of dividing the input random parameter space into smaller subdivision spaces may be as follows:
with parameter intermediate value point V i，center And (3) taking the random parameter space as the center, respectively taking the boundary direction up and down, and dividing the random parameter space according to the sequence from large space to small space to obtain a plurality of subdivision spaces.
The division of the area close to the parameter intermediate value point in the divided subdivision space has a larger division space, and the division of the area far from the parameter intermediate value point has a smaller division granularity, so that the division of the area is smaller.
In high sigma Monte Carlo analysis, the subdivision space near the parameter intermediate value points is not the region of interest, and the subdivision space far from the parameter intermediate value points is the region of interest. Therefore, for the region of interest, the small divided space can increase the sampling density under the same sampling quantity, and samples which do not accord with the performance index can be obtained with higher probability.
Referring to FIG. 4, a schematic diagram of the space division of the input random parameters is shown, where subspace 1, subspace 2 and subspace 3 in FIG. 4 are subdivision spaces after the space division of the input random parameters, wherein the input random parameters P i The upper limit point of the parameter space of (2) is V i，max The lower limit point is V i，min The parameter intermediate value is V i，center 。
Distance V i，center The closer subspace 1 is relatively large, the distance V i，center The further subspace 2 and subspace 3 are relatively small.
Referring to fig. 5, there is shown another schematic diagram of input random parameter space division provided in the embodiment of the present application, and subspace 11, subspace 21, subspace 31, subspace 22 and subspace 32 in fig. 5 are subdivision spaces after input random parameter space division.
Wherein the distance V i，center The nearest subspace 11 is the largest, distance V i，center The closer subspace 21 and subspace 31 are relatively small, distance V i，center The more distant subspace 22 and subspace 32 are relatively smaller.
The spatial division mode provided by the embodiment can further improve the sampling probability of the range sampled by the smaller probability, so that the analysis efficiency and the analysis precision are improved.
In one embodiment of the present application, in order to reduce the dimension of the input random parameter space, the input random parameters with extremely small sensitivity are ignored, and the input random parameters with relatively large sensitivity are reserved, so that the number of the input random parameters is reduced, and the dimension of the input random parameter space is reduced.
The specific process of reducing the dimension of the input random parameter space is as follows:
calculating the sensitivity of each input random parameter corresponding to the performance index of the design object, selecting the input random parameters with the sensitivity larger than or equal to a preset threshold value (namely, filtering out the input random parameters with the sensitivity smaller than the preset threshold value, wherein the input random parameters with the sensitivity smaller than the preset threshold value have little influence on the performance index of the design object), and determining the area range of the selected input random parameters as an integral random parameter space corresponding to the design object, namely, subsequently calculating the design yield according to the reserved parameter space of each input random parameter.
The preset threshold value can be set according to actual requirements, and the invention is not particularly limited.
For ease of understanding the process of simplifying the dimensions of the random parameter space, illustrated herein, for example, the original input random parameter space is (P 1 ，P 2 ，…，P n ) Where n is the original number of input random parameters, i.e. the original parameter space has dimension n, and the degenerated input random parameter space is (P 1 ，P 2 ，…，P m ) The dimension of the new random parameter space is m, m<n。
In the embodiment of the application, the dimension of the input random parameter space is simplified, the parameter space with low dimension is divided, and the efficiency of dividing the random input parameter space is improved.
Based on the method embodiment for obtaining the design yield disclosed in the embodiment of the application, the embodiment of the application also correspondingly discloses the device embodiment for obtaining the design yield.
As shown in fig. 6, the apparatus for obtaining a design yield mainly includes:
the amplifying unit 601 is configured to amplify standard deviations of the input random parameters of the design object, and randomly sample the input random parameters based on the amplified standard deviations to obtain a plurality of first-class sampling points.
Further, the amplifying unit 601 includes:
and the amplifying module is used for amplifying the standard deviation of each input random parameter of the design object by utilizing a plurality of different amplifying factors respectively to obtain a plurality of amplified standard deviations.
And the sampling module is used for randomly sampling each input random parameter based on each amplified standard deviation to obtain a plurality of first-class sampling points.
A first obtaining unit 602, configured to obtain performance measurement values of each first type of sampling point.
A determining unit 603 is configured to determine a first type of input parameter space that may not meet the design criterion based on the performance measurement values and the corresponding performance index values of the first type of sampling points.
Further, the determining unit 603 includes:
the selecting module is used for selecting a sampling point set with the performance measured value within a preset range from the first type of sampling points.
The preset range is obtained according to the performance index value corresponding to the input random parameter.
The corresponding module is used for inputting random parameters to any one of the design objects, and the value range of the random parameters is corresponding to all sampling points in the selected sampling point set.
The first determining module is used for determining the value range corresponding to all the input random parameters in the design object as a first type of input parameter space.
The second obtaining unit 604 is configured to obtain a first class function that characterizes a correspondence relationship between a parameter value that may not meet a design indicator and a performance measurement value, based on a sampling point corresponding to the first class input parameter space and a corresponding performance measurement value corresponding to each input random parameter.
Further, the second obtaining unit 604 includes:
the subdivision module is used for subdividing the first type of input parameter space corresponding to each input random parameter to obtain a plurality of subdivided first type of input parameter spaces.
And the function extraction module is used for carrying out function extraction on sampling points corresponding to the subdivided first-class input parameter spaces and corresponding performance measured values to obtain first-class functions representing the corresponding relation between the parameter values possibly not conforming to the design index and the performance measured values.
The sampling determining unit 605 is configured to perform random sampling to obtain a second type of sampling points based on standard deviation of original distribution of each input random parameter of the design object, calculate the second type of sampling points falling in the first type of input parameter space by using the first type of function to obtain a performance estimation value, and determine a third type of sampling points not conforming to the performance index according to the performance estimation value.
Further, a sampling determination unit 605 for performing random sampling to obtain a second type of sampling point based on standard deviation of original distribution of each input random parameter of the design object, includes:
the judging module is used for randomly sampling any input random parameter of the design object and judging whether the value of the input random parameter is in a first type of input parameter space corresponding to the input random parameter.
And the random sampling module is used for continuing to randomly sample the next input random parameter if the value of the input random parameter is in the corresponding first type of input parameter space.
And the first marking module is used for marking the current sampling point outside the first type of input parameter space if the value of the input random parameter is not in the corresponding first type of input parameter space.
And the second marking module is used for marking the current sampling point in the first type of input parameter space if all the values of the input random parameters of the design object are in the corresponding first type of input parameter space.
Wherein all sampling points marked in the first type input parameter space and all sampling points marked outside the first type output parameter space constitute the second type sampling points.
A calculating unit 606, configured to calculate a design yield in the input random parameter space according to the number of the third type of sampling points and the number of the second type of sampling points.
Further, the calculating unit 606 for determining the overall random parameter space corresponding to the design object includes:
the acquisition module is used for acquiring the sensitivity of each input random parameter corresponding to the design object and selecting the input random parameters with the sensitivity larger than or equal to a preset threshold value.
And the second determining module is used for determining the selected area range of the input random parameters as the whole random parameter space corresponding to the design object.
The embodiment of the application discloses a device for obtaining a design yield, which is used for rapidly positioning a first type of input parameter space which possibly does not accord with a performance index through amplifying standard deviation, obtaining a first type of function based on sampling points corresponding to the first type of input parameter space and corresponding performance measurement values, randomly sampling to obtain a second type of sampling points based on standard deviation of original distribution of each input random parameter of a design object, obtaining a performance estimation value for the second type of sampling points falling in the first type of input parameter space by utilizing the first type of function, determining a third type of sampling points which do not accord with the performance index, and calculating the design yield of the design object according with the performance index in the corresponding overall random parameter space according to the number of the third type of sampling points and the number of the second type of sampling points, so as to improve the efficiency of obtaining the design yield according with the performance index.
As shown in fig. 7, a schematic structural diagram of another device for obtaining a design yield according to an embodiment of the present application is provided, where the device further includes, based on the embodiment shown in fig. 6: the unit 701 is divided.
The dividing unit 701 is configured to divide the overall random parameter space corresponding to the design object to obtain a plurality of subdivision spaces, so as to obtain a second type sampling point and a third type sampling point corresponding to each subdivision space.
Further, the dividing unit 701 is specifically configured to:
and dividing the random parameter space by taking the parameter intermediate value point as a center, respectively taking the boundary direction up and down and according to the sequence from large space to small space, so as to obtain a plurality of subdivision spaces.
Correspondingly, the calculating unit 606 is specifically configured to calculate, based on the number of the second type sampling points, the number of the third type sampling points, and the duty ratio of each subdivision space to the overall random parameter space, a design yield corresponding to the overall random parameter space.
In the embodiment of the application, the random parameter space is divided to obtain a plurality of subdivision spaces, and the design yield corresponding to the whole random parameter space is calculated based on the number of the second type sampling points, the number of the third type sampling points and the ratio of each subdivision space to the whole random parameter space, wherein the second type sampling points and the third type sampling points correspond to each subdivision space. And the number of the second type sampling points and the number of the third type sampling points corresponding to the subdivided space and the duty ratio of the whole random parameter space are utilized, so that the design yield corresponding to the whole random parameter space is obtained more accurately.
For the foregoing method embodiments, for simplicity of explanation, the methodologies are shown as a series of acts, but one of ordinary skill in the art will appreciate that the present invention is not limited by the order of acts, as some steps may, in accordance with the present invention, occur in other orders or concurrently. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present invention.
It should be noted that, in the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described as different from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other. For the apparatus class embodiments, the description is relatively simple as it is substantially similar to the method embodiments, and reference is made to the description of the method embodiments for relevant points.
The steps in the method of the embodiments of the present invention may be sequentially adjusted, combined, and deleted according to actual needs.
The device and the modules and the submodules in the terminal in the embodiments of the invention can be combined, divided and deleted according to actual needs.
In the embodiments provided in the present invention, it should be understood that the disclosed terminal, apparatus and method may be implemented in other manners. For example, the above-described terminal embodiments are merely illustrative, and for example, the division of modules or sub-modules is merely a logical function division, and there may be other manners of division in actual implementation, for example, multiple sub-modules or modules may be combined or integrated into another module, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
The modules or sub-modules illustrated as separate components may or may not be physically separate, and components that are modules or sub-modules may or may not be physical modules or sub-modules, i.e., may be located in one place, or may be distributed over multiple network modules or sub-modules. Some or all of the modules or sub-modules may be selected according to actual needs to achieve the purpose of the embodiment.
In addition, each functional module or sub-module in the embodiments of the present invention may be integrated in one processing module, or each module or sub-module may exist alone physically, or two or more modules or sub-modules may be integrated in one module. The integrated modules or sub-modules may be implemented in hardware or in software functional modules or sub-modules.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (10)
1. A method of obtaining a design yield, the method comprising:
amplifying standard deviation of each input random parameter of the design object, and randomly sampling each input random parameter based on the amplified standard deviation to obtain a plurality of first-class sampling points;
acquiring performance measured values of all the first type sampling points;
Determining a first type of input parameter space which may not meet design indexes based on the performance measurement values of the first type of sampling points and the corresponding performance index values;
obtaining a first class function representing the corresponding relation between the parameter values possibly not conforming to the design index and the performance measured values based on the sampling points corresponding to the first class input parameter space corresponding to each input random parameter and the corresponding performance measured values;
based on standard deviation of original distribution of each input random parameter of a design object, carrying out random sampling to obtain second class sampling points, calculating the second class sampling points falling in the first class input parameter space by using the first class function to obtain a performance estimation value, and determining a third class sampling point which does not accord with a design index according to the performance estimation value;
and calculating the design yield of the design object conforming to the design index in the corresponding overall random parameter space according to the number of the third class sampling points and the number of the second class sampling points.
2. The method of claim 1, wherein the determining a first type of input parameter space that may not meet design criteria based on the performance measure values and corresponding performance index values for the first type of sampling points comprises:
Selecting a sampling point set of which the performance measured value is in a preset range from the first type of sampling points, wherein the preset range is obtained according to the performance index value corresponding to the input random parameter;
inputting random parameters to any one of the design objects, wherein the value range of the random parameters is corresponding to all sampling points in the selected sampling point set;
and determining the value range corresponding to all the input random parameters in the design object as the first type of input parameter space.
3. The method according to claim 1, wherein the obtaining a first class of functions characterizing a correspondence between parameter values that may not meet design criteria and performance measurement values based on sampling points and corresponding performance measurement values corresponding to the first class of input parameter spaces corresponding to respective input random parameters includes:
subdividing the first type input parameter space corresponding to each input random parameter to obtain a plurality of subdivided first type input parameter spaces;
and extracting functions of sampling points corresponding to the subdivided first-class input parameter spaces and corresponding performance measured values to obtain first-class functions representing the corresponding relation between the parameter values possibly not conforming to the design index and the performance measured values.
4. The method of claim 1, further comprising, prior to said amplifying the standard deviation of each of the input random parameters of the design object, randomly sampling each of the input random parameters based on the amplified standard deviation to obtain a plurality of first type sampling points:
dividing the whole random parameter space corresponding to the design object to obtain a plurality of subdivision spaces so as to obtain the second type sampling point and the third type sampling point corresponding to each subdivision space;
the calculating the design yield in the input random parameter space according to the number of the third class sampling points and the number of the second class sampling points comprises the following steps:
and calculating to obtain the design yield corresponding to the whole random parameter space based on the number of the second type sampling points corresponding to each subdivision space, the number of the third type sampling points and the ratio of each subdivision space to the whole random parameter space.
5. The method of claim 4, wherein dividing the random parameter space into the subdivision spaces comprises:
and dividing the random parameter space by taking the parameter intermediate value point as a center, respectively taking the boundary direction up and down and according to the sequence from large space to small space, so as to obtain a plurality of subdivision spaces.
6. The method of claim 1, wherein amplifying the standard deviation of each of the input random parameters of the design object, and randomly sampling each of the input random parameters based on the amplified standard deviation to obtain a plurality of first type sampling points, comprises:
amplifying standard deviations of the random parameters of each input of the design object by utilizing a plurality of different amplification factors respectively to obtain a plurality of amplified standard deviations;
and randomly sampling each input random parameter based on the standard deviation after amplification to obtain a plurality of first-type sampling points.
7. The method of claim 1, wherein determining the overall random parameter space for the design object is as follows:
acquiring the sensitivity of each input random parameter corresponding to the design object, and selecting the input random parameters with the sensitivity greater than or equal to a preset threshold value;
and determining the selected regional range of the input random parameters as an integral random parameter space corresponding to the design object.
8. The method of claim 1, wherein the randomly sampling to obtain the second type of sampling points based on standard deviation of the original distribution of the respective input random parameters of the design object comprises:
Randomly sampling any input random parameter of the design object, and judging whether the value of the input random parameter is in a first type of input parameter space corresponding to the input random parameter;
if the value of the input random parameter is in the corresponding first type of input parameter space, continuing to randomly sample the next input random parameter;
if the value of the input random parameter is not in the corresponding first type of input parameter space, marking the current sampling point outside the first type of input parameter space;
if all the values of the input random parameters of the design object are in the corresponding first type of input parameter space, marking the current sampling point in the first type of input parameter space;
wherein all sampling points marked in the first type of input parameter space and all sampling points marked outside the first type of input parameter space constitute the second type of sampling points.
9. An apparatus for obtaining a design yield, the apparatus comprising:
the amplifying unit is used for amplifying standard deviation of each input random parameter of the design object, and randomly sampling each input random parameter based on the amplified standard deviation to obtain a plurality of first-class sampling points;
The first acquisition unit is used for acquiring the performance measured value of each first type of sampling point;
a determining unit, configured to determine a first type of input parameter space that may not conform to a design index, based on the performance measurement values and the corresponding performance index values of the first type of sampling points;
the second acquisition unit is used for acquiring a first class function representing the corresponding relation between the parameter value possibly not conforming to the design index and the performance measured value based on the sampling point corresponding to the first class input parameter space corresponding to each input random parameter and the corresponding performance measured value;
the sampling determining unit is used for carrying out random sampling to obtain second class sampling points based on standard deviation of original distribution of each input random parameter of the design object, calculating the second class sampling points falling in the first class input parameter space by using the first class function to obtain a performance estimated value, and determining a third class sampling point which does not accord with the performance index according to the performance estimated value;
and the calculating unit is used for calculating the design yield in the input random parameter space according to the number of the third type of sampling points and the number of the second type of sampling points.
10. The apparatus of claim 9, wherein the second acquisition unit comprises:
The subdivision module is used for subdividing the first type of input parameter space corresponding to each input random parameter to obtain a plurality of subdivided first type of input parameter spaces;
and the function extraction module is used for carrying out function extraction on sampling points corresponding to the plurality of subdivided first-class input parameter spaces and corresponding performance measured values to obtain first-class functions representing the corresponding relation between parameter values possibly not conforming to design indexes and the performance measured values.
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