Patent Application
20140156233
I. Field of the Invention
The present invention relates generally to a method and apparatus for the analysis of an electronic circuit.
II. Description of Related Art
As systems containing electronic circuits become increasingly complex, the proper testing of the electronic circuits to ensure satisfactory performance throughout a wide range of operating conditions has become increasingly problematic. For example, a modern automotive vehicle contains many electronic circuits which control the overall operation of the vehicle. These electronic circuits must be tested under a variety of different operating conditions and variations of the electronic circuits. For example, most components in an electronic circuit have a known manufacturing tolerance or known variance in the value of the component. For complete testing of such circuits, it is necessary to accommodate and, if necessary, correct for variations in the values of the electronic components which form the circuit.
Previously, it has been the practice to prototype the circuit for the system so that the circuit has at least one input or independent variable and at least one output or dependent variable. Raw data is then obtained from the prototype by varying the values of the input or independent variables and measuring the effect on the dependent variables. This raw data forms a data set with known inputs or independent variables as well as the outputs or dependent variables over a range of different values for the independent variables.
When obtaining the set of raw data from the circuit under test, it has been the previously known practice to measure the independent and dependent variables for the circuit under a wide variety of environmental conditions. For example, if temperature is suspected as being one of the environmental variables, the circuit testing will be performed under different temperatures to gauge the effect, if any, of temperature differences on the output or dependent variables. Other environmental variables, such as electronic noise, may also be varied under the circuit testing to determine the effect of the environmental factor on the operation of the overall circuit.
Since the raw data is obtained by humans and test measurement, the raw data almost invariably will contain some errors caused by human errors in the testing procedure. Consequently, the raw data is often preprocessed to remove data values that are clearly in error. The preprocessing step, however, is optional but, if performed at the end of the preprocessing step, only valid raw data will remain in the raw data set.
After the invalid data has been removed from the raw data set, the raw data is oftentimes organized for subsequent analysis. For example, optionally the raw data set may be organized through data averaging and/or low-pass filtering in order to facilitate the subsequent analysis of the data. Eventually, however, the remaining raw data in the raw data set will be stored as a document in a computer-accessible file. For example, a spreadsheet is oftentimes used to organize the raw data set in order to simplify its entry to the computer or processor in preparation for the data analysis.
The raw data, optionally preprocessed, is then ready for data analysis. There are many different types of data analysis, such as analysis of variance, T-test, worst-case analysis, regression analysis, etc., which may be performed on the data. Each data analysis method generates its own set of relationships between the independent and dependent variables for the electronic circuit and these relationships between the independent and dependent variables together form a mathematical model with each data analysis method having its own mathematical model. The accuracy of the mathematical model is then determined by inputting the independent variables from the raw data set to the mathematical model and comparing the dependent variables or outputs from the mathematical model with the actual dependent variables in the raw data set. Any difference between the computed data values from the mathematical model and the actual values from the raw data forms an error and the analysis of this error between the computed data values and the raw data is analyzed statistically to determine the accuracy of the selected analysis method for the data.
In some situations, the statistical error of the mathematical model, assuming a sufficient sample size, is sufficiently accurate that the mathematical model can be used for further testing of the electronic circuit. However, in other cases the selected analysis method produces an unacceptable error between the computed dependent variables and the measured variables from the raw data. When this occurs, it is necessary to select a different analysis method and apply that different analysis method to the raw data until a mathematical model having an acceptable error between the computed data and the raw data is identified. That procedure, however, is not only time consuming, but cumbersome.
The present invention provides a method and apparatus using a programmed processor for electronic circuit simulation which overcomes the above-mentioned disadvantages of the previously known methods.
In brief, in the method of the present invention raw data is first collected from the circuit to form a raw data set of the independent or input variables and the dependent or output variables. The raw data set is taken, if applicable, under different environmental conditions, such as different temperatures, random electrical noise, etc., of the type that the electronic circuit would be subjected to during normal operation.
Optionally, the raw data is preprocessed to remove incorrect data. Conventional preprocessing methods may be used, such as data averaging and low-pass filtering. The resulting raw data, whether preprocessed or not, is then organized into a computer-readable file, such as a spreadsheet.
Unlike the previously known methods for circuit simulation, the raw data is then analyzed using at least two, and preferably more, data analysis methods to generate the relationships between the dependent and independent variables of the circuit. These data analysis methods can include, for example, ANOVA (analysis of variance) analysis method, T-test method of analysis, worst-case analysis, and the like. In each case, the analysis method generates its own relationships between the independent and dependent variables for the electronic circuit.
Using the relationships between the independent and dependent variables, each analysis method constructs a mathematical model which defines the relationship between the dependent and independent variables. The mathematical model will differ from one analysis method to the other and can include, for example, first order equations, second order equations, higher order equations, differential equations, and the like.
After the mathematical models are constructed for each analysis method, the accuracy of each model is then statistically determined. This is accomplished by utilizing the same set of independent variables in the raw data set and computing the dependent variables as a function of the mathematical model. Unless the computed values for the dependent variables in the mathematical model are identical to the actual data in the raw data set, which is rarely the case, an error will exist between the computed data for the dependent variables and the actual raw data for the dependent variables for the various values of the independent variables in both the raw data and the mathematical model.
Assuming that a sufficient number of data for the model exists to provide an accurate representation, the accuracy of the mathematical model is then determined using standard statistical techniques. For example, the standard deviation for each model can be determined to ensure that the mathematical model meets the minimum accuracy requirements for the circuit application.
The accuracies for the different mathematical models for the different analysis methods are then compared and the mathematical model having the highest statistical accuracy is then selected by the programmed processor provided that the sample size for that particular mathematical model meets the minimum requirements for statistical reliability.
Although conventional mathematical methods may be utilized to select the analysis method and its resulting mathematical model with the highest statistical accuracy, optionally fuzzy logic is employed to make that selection. The reasoning logic to select the most accurate mathematical model and analysis method also preferably accesses a computer expert database containing known data for the accuracy of the analysis methods as well as the required sample sizes.
After selection of the mathematical model and analysis method exhibiting the highest accuracy for the simulation, a simulation model is preferably constructed for the electronic circuit. Any conventional method, such as Spice, PSpice, Saber, and so forth may be used to construct the simulated circuit and facilitate further testing of the circuit.
A better understanding of the present invention will be had upon reference to the following detailed description when read in conjunction with the accompanying drawing, wherein like reference characters refer to like parts throughout the several views, and in which:
With reference first to
With reference now to
In addition to simply varying the value of the inputs at step 22, the performance of the circuit may also vary as a function of environmental conditions. For example, the temperature of the circuit, presence of electronic noise, and the like may vary the performance of the circuit. Consequently, the collection of the raw data into a raw data set at step 22 may be conducted under different environmental conditions and these different environmental conditions also become a part of the raw data set with the environmental condition, such as temperature, constituting a separate “input” or independent variable for the electronic circuit.
After the raw data has been manually collected at step 22, step 22 proceeds to step 24 where the raw data is optionally preprocessed to remove erroneous data. Since the raw data was manually collected at step 22, the raw data inevitably includes some human error. The optional preprocessing step 24 may be used to eliminate, or at least reduce, that human error.
Any conventional preprocessing may be used to ensure that the raw data is valid data. For example, data averaging or low-pass filtering may be applied to the raw data in order to remove data anomalies which represent invalid data. However, the data remaining after the preprocessing step 24, if performed, still remains raw data. Step 24 then proceeds to step 26 where the raw data is analyzed.
With reference now to
More specifically, at step 28 a first method of analysis, e.g. ANOVA, is performed on the raw data. As a function of that analysis using conventional analysis tools, the analysis methods generate their results at step 30. The results generated at step 30, furthermore, consist of mathematical relationships between the inputs or independent variables and the outputs or dependent variables for the electronic circuit. These mathematical formulas may take a variety of different forms, such as first, second, or even higher order equations, exponential equations, differential equations and the like. The results generated at step 30 are stored by the processor in computer memory or other storage medium. Step 30 then proceeds to step 32.
At step 32 the raw data is again analyzed by using a different method of analysis. Using the different method of analysis, step 32 proceeds to step 34 where the second method of analysis generates mathematical relationships between the inputs or independent variables and the outputs or dependent variables for the circuit. As before, these relationships may be of any form, such as first, second, or even higher order equations, exponential equations, differential equations and the like.
The above process, i.e. analysis of the raw data by a different analysis method and the generation of the mathematical relationships between the inputs and outputs for the circuit, proceeds through analysis method N at steps 36 and 38.
After all of the analysis methods have been applied to the same raw data, each analysis method generating its own set of mathematical relationships between the inputs and the outputs, the data analysis step 26 proceeds to step 40 as shown in both
At step 40, the mathematical relationships between the dependent and independent variables for each different mathematical model, one model associated with each different analysis method, are grouped to create the mathematical model. The number of different relationships or equations for each different mathematical model will differ. However, ideally each mathematical model will be able to predict the value of its outputs or dependent variables as a function of the circuit inputs or independent variables with only a minimum of error. An exemplary flowchart illustrating the calculation of the error of the mathematical model is shown in
With reference then to
Step 44 then calculates or computes the value of the dependent variables using the raw data input values and the mathematical formulae of the mathematical model which correspond to the analysis method of steps 28 and 30 (
At step 48, the processor determines whether or not all of the inputs of the raw data set have been analyzed for the first analysis method. If not, step 48 branches back to step 42 where the above analysis is repeated for the next data input of the raw data set. Otherwise, step 48 proceeds to step 50. At step 50 the program determines whether or not all of the different analysis methods, i.e. analysis method 1 through analysis method N, have been analyzed to compute the error between the mathematical model dependent variables and the outputs from the raw data in the raw data set. If not, step 50 branches back to step 42 and reiterates the above-described process for the next analysis method. Conversely, if all the analysis methods have been analyzed and the errors determined and stored by the processor, step 50 proceeds to step 52 which is the reasoning module to select the best analysis method for the particular circuit under examination.
With reference now to
In the logic decision engine 56, a primary factor considered by the logic decision engine 56 is the error P(x) between the raw data output values and the computed values from the mathematical model for a given set of input or independent variables. For example, as shown in
For example, as shown in
After the accuracy of all of the analysis methods has been calculated, the logic decision engine 56 assigns an index to each method on a 100% scale with the highest index given to the most accurate mathematical model and vice versa. These index numbers, however, are also subject to input from the expert database 54 to ensure that an adequate number of samples are available for that particular analysis method. If not, a very low index is associated with that particular analysis method.
Although conventional statistical analysis may be applied to determine which of the different analysis methods is the most accurate, the logic decision engine 56 may use fuzzy logic in its determination of the most accurate analysis method. More specifically, the data from the mathematical models is inputted by the logic decision engine 56 at step 60. Step 60 then proceeds to step 62.
With reference to
At step 64 the processor using the fuzzy rules determined at step 62 creates the fuzzy inferences for the data. Step 64 then proceeds to step 66.
At step 66, the processor performs defuzzification on the data after processing with the fuzzy inference at step 64. Any of several methods may be used to perform defuzzification. For example, centroid of area, maximum, bisector of area, mean of maximum, smallest of maximum, and largest of maximum are all different defuzzification methods used to obtain a crisp output. Any of these methods may be used to perform defuzzification in the instant invention and, preferably, the database 54 of expert experience contains an identification of which defuzzification method should be used to obtain optimal accuracy of the mathematical model.
With reference again to
With reference now to
Still referring to
One or more input devices 108 also communicate with the CPU to provide the required input data, such as the raw data from the circuit, to the CPU 100. Typically, the CPU 100 will store this raw data as well as other input data in the storage memory block 104 for subsequent use by the CPU 100 when required. Furthermore, although the input device 108 may be a direct input device, such as a keyboard, more typically the input device 108 comprises a computer file which may be inputted by the CPU 100.
Lastly, the CPU communicates with one or more output devices 110 for use by the operator of the system. The output devices 110 may comprise, for example, video displays, printers, and/or the like.
In practice, the CPU 100 is programmed to execute the program illustrated in
Thereafter, under program control from the program memory block 102, the CPU generates the mathematical modeling at step 40 for each different method under program control of the method illustrated more fully in
From the foregoing, it can be seen that the present invention provides a circuit simulation method executed by a programmed processor which not only automates the analysis and mathematical modeling of the electronic circuit, but also automatically selects which of two or more analysis methods provides the optimal result from the simulation. Having described our invention, however, many modifications thereto will become apparent to those skilled in the art to which it pertains without deviation from the spirit of the invention as defined by the scope of the appended claims.
