Anritsu机器学习应用于PCB外观检测.pdf - 第2页

Anritsu T echnical Revi ew No.28 September 2020 Application of M achine Learni ng to Printed C ircuit Board External In spection (2) parts to the board in a sin gle automated process. G enerally , technology for produc i…

Application of Machine Learning to Printed Circuit

Board External Inspection

Ken Shioiri, Shoji Hamao

[Summary] External inspection of solder joints. etc., at parts mounting on printed-circuit boards is a key part

of assuring reliability. Visual inspection by eye is a fundamental inspection method, but it is diffi-

cult to secure well-trained people who can do the easy work, but which requires some training be-

fore becoming proficient board inspectors. In addition, the aging population in Japan is experi-

encing loss of veterans to train new younger staff. At external inspection, automation is combined

with image processing, but mistakes are common and final visual confirmation by eye is often re-

quired. Improved inspection performance is anticipated following recent advances in im-

age-technologies using machine learning

1), 2), 3), 4)

. This article introduces automation of external

inspection using machine learning.

(1)

1 Introduction

External inspection of products during manufacturing

processes involves visual inspection by people as well as

automatic inspection using image processing. Since visual

inspection by people relies upon pass/fail judgements using

human senses, inspectors can easily make human errors

due to randomness in the evaluation criteria. In addition,

inspection results can be impacted by the inspection envi-

ronment, such as illumination levels at the inspection site.

Psychological and physical factors can also create differ-

ences in work accuracy and speed. Further, parts are be-

coming smaller and higher density with rising parts quality

requirements. Moreover, small-lot production to meet cus-

tomers' diversifying needs is also increasing and product

life-cycles are becoming shorter as customers' requirements

change more frequently. These are increasing the work

burden on quality assurance management.

Consequently, the current focus is on automation of ex-

ternal inspection using cameras. If camera-based inspection

processes can be automated, problems with human errors,

randomness in inspection criteria, worker recruitment,

training, and labor costs can be eliminated. In addition,

automation can increase in-line inspection speeds to im-

prove mass production efficiency while assuring product

quality.

There are two methods for automating inspection—using

evaluation criteria determined either by people, or by ma-

chine learning. The former method is becoming more com-

plex due to the diversification of part types requiring more

evaluation criteria, which are difficult to manage and keep

corrected. The latter machine-learning method is expected

to yield high evaluation performance with sufficient num-

bers of sample images and correct label information (indi-

cating whether product image is pass or fail, and solving

questions, such as whether part position in image is bad,

etc.). External inspection using images is becoming in-

creasingly mainstream with the development of ma-

chine-learning technologies, and we expect more cases for

development of inspection algorithms for imaging solving

mass-production line problems.

This article outlines printed circuit board (PC board)

manufacturing processes in section 2, typical solder faults

in section 3, issues in AOI external inspection of PC boards

in section 4, PC board inspection using machine learning in

section 5, and evaluation of PC board inspection results

using machine learning in section 6.

2 Outline of PC Board Manufacturing

At manufacturing of general-purpose surface-mount PC

boards, solder paste

Note 1

is applied to the PC board using a

liquid presoldering method. As shown in Figure 1, using the

pre-soldering method, first, solder paste is painted onto the

board surface through a stencil-like metal mask

Note 2

(Solder

Paste Printing). This is followed by application of an adhe-

sive to prevent parts falling off during mounting and then

by mounting of parts on the board surface. Next, the pasted

board populated with parts passes through the reflow pro-

cess to melt and solidify the solder paste and secure the

Anritsu Technical Review No.28 September 2020 Application of Machine Learning to Printed Circuit Board External Inspection

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parts to the board in a single automated process. Generally,

technology for producing a PC board with surface-mounted

parts is called Surface Mount Technology (SMT). As the PC

boards emerge from the reflow oven, the external inspection

check is performed to make sure there are no problems with

the alignment and soldering of parts on boards. This ex-

ternal inspection check can be performed by either people

using the naked eye or a microscope, or by Automated Op-

tical Inspection (AOI) using optical imaging.

At inspection by AOI, an image of the PC board is cap-

tured from a fixed reference position and the solder condi-

tion of each part is assessed from the image; the presence or

absence of parts, their angles, and the part number (printed

on part), etc., are checked to evaluate whether the PC board

is pass or fail. An error report listing the nature of the faulty

part and faulty board is output for failing boards and these

boards are finally checked visually by people to confirm the

fault evaluation.

Since the AOI camera shot is taken from a fixed reference

position, sometimes it may be difficult to visualize the part

leads and electrodes, making it difficult to accurately assess

the part solder conditions. Consequently, PC boards with

parts that cannot be inspected by AOI must depend on in-

spection by human eye.

Recently, chip mounting densities are increasing using

many parts as small as 1.0 × 0.5 mm and 0.6 × 0.3 mm, re-

quiring even more accurate and faster processing using AOI.

Note 1: Solder paste is a high-viscosity mixture of flux and solder

balls with diameters of 20 to 30 µm. It is called solder cream.

Note 2: A solder mask is a stainless-steel stencil sheet that is over-

laid on the PC board; the required amount of solder paste is

painted accurately at required positions on the PC board

through holes cut in the mask.

3 Typical Solder Errors

When parts are mounted correctly, the part lead or elec-

trode is soldered cleanly to the land on the PC board

Note 3

and the external appearance of the joint is a smoothly

curved meniscus. This is described as a fillet. In addition,

the joint surface is "flowing" and "wets" the connected ele-

ments. Figure 2 shows some examples of correct fillet

shapes for solder joints.

If there are any problems with applied solder paste

amount or position, position of mounted parts, or with the

reflow temperature management, the fillet shape may be

deformed, resulting in a poor solder joint between the part

and board. See reference 5 for details of the causes of vari-

ous types of poor SMT joints. Some typical examples are

listed below and shown in Figure 3.

Inspector

Solder

Paste

Adhesi-

Printer

Reflow Oven

External

Inspector

(AOI)

AOI

Terminal

Parts Mounting

PC Board Progress

Reflow

External Check

Solder Paste

Printing and Check

Parts Mounters several units

Adhesive

PC Board

To Next

Process

Visual Inspection

Figure 1 Example of SMT Line

PC Board

Lead

Fillet

Land

Fillet

Land

Electrode

Lead

Fillet

Land

PC Board

(a) Chip Parts (b) SOP and QFP Parts

Figure 2 Examples of Normal Fillet Shapes

Anritsu Technical Review No.28 September 2020 Application of Machine Learning to Printed Circuit Board External Inspection

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Figure 3 Examples of SMT Mounting Faults

(1) Poor wetting

The solder is not spread evenly in the correct amount over

the part leads and electrodes and the fillet form does not

appear wetted. The causes are insufficient solder, leads or

electrodes not in contact with land, and insufficient heat at

reflow.

(2) Solder bridging (shorts)

This problem tends to occur when using very small SOP

and QFP ICs with sizes of 0.5 mm and 0.4 mm

Note 4

or when

the correct amount of solder is not applied to adjacent leads.

The causes are poor solder printing, bent solder leads, and

poor parts mounting.

(3) Vertical chip (gravestone and Manhattan)

This problem occurs when both electrodes of a part are

not soldered simultaneously and surface tension at the end

wetted first causes the chip to stand vertically on one sol-

dered electrode.

The countermeasures are improving the land dimensions

and mounting accuracy, and preheating to reduce the solder

melt time difference.

(4) Non-contact, missing solder

This is caused by the solder paste not melting during re-

flow and remaining in a paste state. The causes are old

solder paste or poor reflow oven temperature control.

Missing solder is caused by lack of solder at the part and

is caused by poor solder paste printing.

(5) Rotated and slipped parts

Rotated or slipped parts are the result of part leads or

electrodes projecting outside the land or from poor posi-

tioning by the parts mounter. Vibration at mounting other

parts or at conveying/transport between processes can re-

sult in surface tension issues causing displacement as in

vertical chip faults.

Note 3: The land is a part where the copper forming the PC board

traces is exposed for soldering to the part leads and elec-

trodes. Sometimes the land surface is gold-plated.

Note 4: SOP and QFP describe IC packages. A SOP (Small Outline

Package) has L-shaped legs from two sides of the rectan-

gular package connecting to lands. A QFP (Quad Flat

Package) has multiple leads on all four sides of the package

connecting to lands.

4 AOI PC Board External Inspection Issues

AOI inspection sets strict evaluation criteria using mul-

tiple parameters so as to not allow fail products to pass in-

spection. As a result, so-called "excess watching" is a com-

mon problem. Excess watching is a phenomenon where

products passing at the visual-inspection level are evalu-

ated as fail by the AOI inspection system. If there is too

much excess watching, visual confirmation after automatic

inspection is increased.

To proactively suppress excess watching, the soldered

part digital evaluation criteria are readjusted repeatedly

over but there can be a problem where excess watching does

not decrease because fine-adjustment cannot be completed

due to momentary changes in the processing conditions

caused by PC board condition and parts mounting ran-

domness.

5 PC Board External Inspection using Machine

Learning

Applying machine learning to the visual confirmation

work following AOI inspection has helped with excess

watching inspection efficiency.

5.1 Leaned Image Data Acquisition

Collecting and annotating (labeling) images used for

machine learning is a key process in applying machine

learning. Consequently, we configured a system (Figure 4)

to capture AOI inspection image data for use as learning

images.

Generally, dedicated terminals are required to capture

AOI inspection results and inspection image data. In this

development, we obtained the terminal interface specifica-

tions from the AOI maker and developed software to di-

rectly capture inspection results and learning images (in-

spection images) at a connected personal computer (PC).

(a) Poor wetting

(b) Solder Bridge

(d) Missing Solder

(e) Rotated Part