Method of optimizing parameters of electronic components on printed circuit boards

In a method of optimizing parameters of electronic components on printed circuit boards (PCBs), a first experiment table for m variables of one type of parameter of P electronic components on a PCB is designed using n values of each variable and the RSM. P EHs of each first experiment are obtained by simulating, and P EH empirical formulas are computed according to the P EHs. A second experiment table for the m variables is designed using n′ values of each variable and the full factorial design, and P EHs of each second experiment are computed using the P EH empirical formulas. Experiments, all the P EHs of which are greater than 1, are filtered from the second experiment tables, and an average EH of each filtered experiment is computed to pick an experiment the average EH of which is the greatest. The values of the m variables in the picked experiment are considered as optimized.

BACKGROUND

1. Technical Field

Embodiments of the present disclosure generally relate to design of printed circuit boards (PCB)s, and more particularly to a method of optimizing parameters, which influence signal transmission, of electronic components on 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 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.

Parameters of electronic components on a PCB, such as lengths of wiring lines, wiring layers, and characteristic impedance of transmission lines, will influence the design of the PCB. Thus, how to optimize the parameters is important.

DETAILED DESCRIPTION

As mentioned above, parameters of electronic components on a PCB2(shown inFIG. 2), such as lengths of wiring lines, wiring layers, and characteristic impedance of transmission lines, will influence the design of the PCB2. Each parameter may have m variables, and each variable may have n values, where both m and n are positive integers. Such as the example illustrated inFIG. 1AandFIG. 1B, the parameters, namely the lengths of wiring lines of 9 memories D0˜D8have 4 variables A, B, C, and D, and each variable has 3 different values represented as “1”, “0”, and “−1”. In order to disclose the method of optimizing parameters of electronic components on PCBs comprehensibly, the following paragraphs give a detailed description of optimizing the 4 variables A, B, C, and D of the lengths of wiring lines of 9 memories D0˜D8referring toFIG. 3˜FIG.10. However, it should be understood that other parameters can be analyzed in a similar method.

FIG. 3is a flowchart illustrating a method of optimizing parameters of electronic components on PCBs, according to embodiments of the present disclosure. The method is implemented by execution of computer readable program code by at least one processor of a computing device (not shown), such as a computer. Depending on the embodiment, additional blocks in the flow ofFIG. 3may be added, others removed, and the ordering of the blocks may be changed. As shown inFIG. 2, the PCB2is electronically connected to a computing device1. The computer1includes a optimization module10, a processor11, and a storage system12. It may be understood that one or more specialized or general purpose processors, such as the processor11, may be used to execute one or more computerized codes of the optimization module10. The one or more computerized codes of the optimization module10may be stored in the storage system12. The storage system12may be a hard disk drive, flash memory, or other memory to store various data, such as test results, for example.

In block S10, the optimization module10obtains m variables of one type of parameters of P electronic components on the PCB2, where both m and P are positive integers. Referring toFIG. 1A, in the present embodiment, m is 4, and the m variables include A, B, C, and D. P is 9, and the parameters of the P electronic components are the lengths of wiring lines of 9 memories on the PCB2.

In block S11, the optimization module10sets n values for each of the m variables, where n is a positive integer. Referring toFIG. 1B, in the present embodiment, n is 3, and the n values of each of the m variables are “1”, “0”, and “−1”. In one example, the optimization module10can set “3000”, “2500”, and “2000” as three values for the variable A, sets “650”, “550”, and “450” as three values for the variable B, and so on.

In block S12, the optimization module10designs a first experiment table for the m variables using the n values of each of the m variables and the Response Surface Methodology (RSM). In some embodiments, the first experiment table may include n groups of experiment data of first experiments. Referring toFIG. 4(A)andFIG. 4(B), using RSM, the optimization module10firstly constructs a cube whose axis are respectively any three of the m variables, and then designs the first experiment table using, for example, eight vertexes and the center point of the cube.

In block S13, referring toFIG. 5, the optimization module10obtains P eye heights (EH)s of each of the first experiment s by simulation. It may be understood that, the P EHs are respectively EHs of a digital data signal generated by the P electronic components. For example, in the present embodiment, each EH0inFIG. 4is the EH of a digital data signal generated by the first memory D0in different first experiment. In telecommunication, an eye pattern, also known as an eye diagram, is an oscilloscope display in which a digital data signal from a receiver is repetitively sampled and applied to the vertical input, while the data rate is used to trigger the horizontal sweep. It is called as eye pattern because, for several types of coding, the pattern looks like a series of eyes between a pair of rails. There are many measurements that can be obtained from an eye pattern, one of which is the eye height.

In block S14, the optimization module10computes P EH empirical formulas according to the P EHs obtained from each of the first experiments using a predetermined model formula and mathematical regression analysis. It may be understood that, the P EH empirical formulas are respectively used to compute EHs of the digital data signal generated by the P electronic components. Referring toFIG. 6(A), one example of the predetermined model formula is:
EH=b0+b1*A+b2*B+b3*C+b4*D+b5*A2+b6*B2+b7*C2+ . . . +bm*AB+bm+1*AC+ . . . +e.
In the model formula, b0, b1, . . . bm+1, are coefficients computed by the computing device. Referring toFIG. 6(B), the P EH empirical formulas are:
EH1=0.902702+0.008667*A+0.013161*B−0.02782*C−0.00031*D+0.100697*A2+0.006947*B2−0.01425*C2+ . . . −0.00064*AB+0.000687*AC+ . . . +e;
EH2=0.924222+0.002828*A−0.01623*B−0.04301*C+0.000311*D+0.082787*A2+0.018337*B2−0.03501*C2+ . . . +0.000063*AB+0.000225*AC+ . . . +e; and so on.

In block S15, referring toFIG. 7(A), the optimization module10resets n′ values for each of the m variables, where n′ is a positive integer, and n′ is greater than n. In the present embodiment, the n′ values of each of the m variables are represented by “1”, “0.5”, “0”, “−0.5”, and “−1”. For example, the optimization module10sets “3000”, “2750”, “2500”, “2250”, and “2000” five values for the variable A, sets “650”, “600”, “550”, “500”, and “450” five values for the variable B, and so on.

In block S16, referring toFIG. 7(B), the optimization module10designs a second experiment table for the m variables using the n′ values of each of the m variables and the full factorial design. In some embodiments, the second experiment table may include n′ groups of experiment data of second experiments. In statistics, the full factorial design is an experiment whose design consists of two or more factors, each with discrete possible values or “levels”, and whose experimental units take on all possible combinations of these levels across all such factors.

In block S17, referring toFIG. 8, the optimization module10computes P EHs of each of the second experiment table using the P EH empirical formulas.

In block S18, the optimization module10filters a plurality of experiments, all the P EHs of which are greater than 1, from the second experiment tables, such as the experiments whose status are PASS inFIG. 9.

In block S19, the optimization module10computes an average EH of the P EHs of each of the filtered experiments.

In block S20, the optimization module10selects an experiment, which has the greatest average EH, from the filtered experiment tables, and regards the values of the m variables in the selected experiment as the optimal variables of the parameters. Referring toFIG. 10, the average EH of the experiment corresponding to the values of the variables A, B, C, and D represented by “−1”, “1”, “−1”, and “−1”, is the greatest EH, thus, in the present embodiment, the optimal values of the 4 variables A, B, C, and D of the lengths of wiring lines of 9 memories D0˜D8are respectively 2000, 650, 900, and 300.