Abstract:
In a method of managing process factors that influence electrical properties of printed circuit boards (PCBs), n process factors are arranged in an order according to different influence to one kind of electrical property of the PCBs. The different influence is determined by first experiments designed using the Taguchi method. M process factors that have important influence to the electrical property are obtained from the n process factors according to the order to design second experiments. A computing formula for the electrical property is fitted using the m process factors according to simulated results of the second experiments, and a variation range of each of the m process factors is computed according to the computing formula.

Description:
BACKGROUND 
     1. Technical Field 
     Embodiments of the present disclosure generally relate to design of printed circuit boards (PCBs), and more particularly to a method of managing process factors that influence electrical properties of PCBs. 
     2. Description of Related Art 
     Quality is an important factor in the manufacture of electronic products. It may be understood that, quality of electronic products may be determined at the design stage. A printed circuit board (PCB) is used to mechanically support and electrically connect electronic components using conductive pathways, tracks or signal traces etched from copper sheets laminated onto a non-conductive substrate. Thus, design of the PCB plays an important role in the quality of an electronic device which houses the PCB. 
     The design of the PCB usually includes taking into account certain physical parameters to achieve desired electrical properties. The parameters may include physical sizes of the electronic components and signal lines on the PCB, such as line-width, line-spacing, and line-length. The desired properties may include impedance characteristics, and voltage and amplitude variations of electrical signals. The design of PCBs while taking into account the parameters and properties is a complex and crucial process that influences the quality of an electronic device installed with the PCB. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a flowchart illustrating a method of managing process factors that influence electrical properties of PCBs, according to embodiments of the present disclosure. 
         FIG. 2  is a block diagram of one embodiment of a computing device comprising a managing module. 
         FIG. 3  is a table comprising exemplary enumerated measurements of one kind of electrical property of N PCBs, and characteristic values obtained from the measurements, according to embodiments of the present disclosure. 
         FIG. 4  is a table comprising exemplary enumerating n process factors that influence the electrical property of  FIG. 2 , according to embodiments of the present disclosure. 
         FIG. 5  is a table illustrating first experiments designed using the Taguchi method, according to embodiments of the present disclosure. 
         FIG. 6  uses graphs to illustrate different influences of the n process factors to the electrical property of  FIG. 3 , according to embodiments of the present disclosure. 
         FIG. 7  is an example illustrating an order of the n process factors, arranged according to the graphs of  FIG. 6 , according to embodiments of the present disclosure. 
         FIG. 8A  and  FIG. 8B  illustrate second experiments designed using the Response surface methodology, according to embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     The application is illustrated by way of examples and not by way of limitation in the figures of the accompanying drawings in which like references indicate similar elements. It should be noted that references to “an” or “one” embodiment in this disclosure are not necessarily to the same embodiment, and such references mean at least one. 
       FIG. 1  is a flowchart illustrating a method of managing process factors that influence electrical properties of PCBs  2 , according to embodiments of the present disclosure. Depending on the embodiment, additional blocks in the flow of  FIG. 1  may be added, others removed, and the ordering of the blocks may be changed. As shown in  FIG. 2 , the PCBs  2  are electronically connected with a computing device  1 . The computer  1  includes a managing module  10 , a processor  11 , and a storage system  12 . It may be understood that one or more specialized or general purpose processors, such as the processor  11 , may be used to execute one or more computerized codes of the managing module  10 . The one or more computerized codes of the managing module  10  may be stored in the storage system  12 . The storage system  12  also stores various data, such as test results, for example. 
     Types of the process factors that influence electrical properties of each of the PCB  2  may include differential impedance of signal lines, voltage variation, and amplitude variation of electrical signals, for example. In order to describe the below embodiments conveniently, the electrical properties discussed are in regards to the differential impedances as an example. However, it should be understood other electrical properties as mentioned above may be analyzed in substantially the same process. 
     In block S 10 , referring to a table given in  FIG. 3 , the managing module  10  measures differential impedances of N number of PCBs  2 , where N is a positive integer. In the present embodiment, N is 100. The differential impedances of the PCBs  2  can include one or more components, traces, signals of the PCB  2  or the PCB  2  itself. 
     In block S 11 , the managing module  10  analyzes the measurements to obtain characteristic values from the measurements, like the table illustrated in  FIG. 3 . In the present embodiment, the characteristic values include an average value of the measurements, a standard deviation of the measurements, a maximum value of the measurements, a minimum value of the measurements, and a variation range of the measurements. 
     In block S 12 , referring to a table given in  FIG. 4 , the managing module  10  selects n process factors that can influence the differential impedance, where n is a positive integer. In the present embodiment, n is 7, and the n process factors includes “W”, “S”, “t”, “SR”, “Er”, “D 2 ”, and “D 1 ”. In one example, “W” is a width of differential signal, “S” is a spacing between differential signal wires, “t” is a thickness of copper foil of the differential signal wires, “SR” is a shrunk ratio of each of the PCBs  2 , “ER” is a dielectric constant of each of the PCBs  2 , “D 1 ” is an upper layer dielectric height, and “D 2 ” is a low layer dielectric height. 
     In block S 13 , referring to the example of  FIG. 4 , the managing module  10  computes a maximum value and a minimum value for each of the n process factors according to a corresponding normal value (middle value) of each process factor using a predetermined variation percentage. It may be understood that, the differential impedance will reach its target value when each process factor is valued at the corresponding normal value. In the present embodiment, the predetermined variation percentage is 10%. 
     In block S 14 , referring to an example illustrated in  FIG. 5 , the managing module  10  designs first experiments for the n process factors using the Taguchi method, substitutes the maximum value, the middle value, and the minimum value of each process factor into the first experiments, and computes a simulated result of the differential impedances of each of the first experiments. 
     In block S 15 , the managing module  10  computes an average simulated result of each of the maximum value, the middle value, and the minimum value of each process factor. In an example, referring to  FIG. 5 , experiments  01 ˜ 09  use the maximum value of the process factor “W”, experiments  10 ˜ 18  use the middle value of the process factor “W”, and the experiments  19 ˜ 27  use the minimum value of the process factor “W”. The managing module  10  totals the simulated results of the experiments  01 ˜ 09 , and divides the total simulated results by 9 as the average simulated result of the maximum value of the process factor “W”. Furthermore, the managing module  10  totals the simulated results of the experiments  10 ˜ 18 , and divides the total simulated results by 9 as the average simulated result of the middle value of the process factor “W”. The managing module  10  then totals the simulated results of the experiments  19 ˜ 27 , and divides the total simulated results by 9 as the average simulated result of the minimum value of the process factor “W”. In another example, still referring to  FIG. 5 , experiments  01 ˜ 03 ,  10 ˜ 12 , and  19 ˜ 21  use the maximum value of the process factor “S”, experiments  04 ˜ 06 ,  13 ˜ 15 , and  22 ˜ 24  use the middle value of the process factor “S”, and the experiments  07 ˜ 19 ,  16 ˜ 18 , and  25 ˜ 27  use the minimum value of the process factor “S”. The managing module  10  totals the simulated results of the experiments  01 ˜ 03 ,  10 ˜ 12 , and  19 ˜ 21 , and divides the total simulated results by 9 as the average simulated result of the maximum value of the process factor “S”. Furthermore, the managing module  10  totals the simulated results of the experiments  04 ˜ 06 ,  13 ˜ 15 , and  22 ˜ 24 , and divides the total simulated results by 9 as the average simulated result of the middle value of the process factor “S”. The managing module  10  then totals the simulated results of the experiments  07 ˜ 19 ,  16 ˜ 18 , and  25 ˜ 27 , and divides the total simulated results by 9 as the average simulated result of the minimum value of the process factor “S”. 
     In block S 16 , referring to  FIG. 6  and  FIG. 7 , the managing module  10  computes a variation range of the average simulated results of the maximum value, the middle value, and the minimum value of each process factor, and puts the n process factors into an order according to the variation ranges. In an example, referring to  FIG. 6  and  FIG. 7 , the average simulated result of the maximum value 4.4 of the process factor “W” is 94.43, the average simulated result of the middle value 4 of the process factor “W” is 98.26, and the average simulated result of the minimum value 3.6 of the process factor “W” is 102.4 Therefore, the variation range of the average simulated results of the maximum value, the middle value, and the minimum value of process factor “W” is 7.96. It may be understood that, the order shows that different process factors have different influence on the differential impedances of PCBs  2 . 
     In block S 17 , the managing module  10  determines m process factors which have influence on the differential impedances from the n process factors according to the order, where m is a positive integer. In the present embodiment, m is 3. Referring to the order in  FIG. 7 , the m process factors are “Er”, “W”, and “D 1 ”. 
     In block S 18 , referring to an example given in  FIG. 8A  and  FIG. 8B , the managing module  10  designs second experiments for the m process factors using Response Surface Methodology (RSM), substitutes the maximum value, the middle value, and the minimum value of each of the m process factors, and the middle value of each of the other (n−m) process factors into the second experiments, and computes a simulated result of the differential impedance of each second experiment. Referring to  FIG. 8A , using RSM, the managing module  10  firstly constructs a cube whose axis are “Er”, “W”, and “D 1 ”, and then designs the second experiments using the center points of the cube and twelve edges of the cube. 
     In block S 19 , the managing module  10  fits a computing formula for the differential impedance using the m process factors, according to the second experiments and the simulated results of the differential impedance of the second experiments. One example of the computing formula is as follows:
 
 Z diff=267.685−14.684 *W− 17.2189 *Er− 26.0471 *D 1+0.323947 *W 2+0.452506 *Er 2+1.01153 *D 12+0.958375 *WEr+ 0.606312 *WD 1+0.776867 *ErD 1.
 
     In block S 20 , the managing module  10  substitutes the characteristic values into the computing formula, to compute a variation range of each of the m process factors. As mentioned in block S 11 , the characteristic values include the average value of the measurements, the standard deviation of the measurements, the maximum value of the measurements, the minimum value of the measurements, and the variation range of the measurements. 
     It may be understood that, using the variation range of each of the m process factors, an electrical property, such as the differential impedance of each of the PCBs  2  can be well controlled. Thus, the quality of an electronic device using each of the PCBs  2  will be improved. 
     Although certain inventive embodiments of the present disclosure have been specifically described, the present disclosure is not to be construed as being limited thereto. Various changes or modifications may be made to the present disclosure beyond departing from the scope and spirit of the present disclosure.