editorial digest - vision-systems.com · editorial digest - vision-systems.com

Editorial digEst

Machine Vision in solar Cell ManufacturingWith demand for more efficiency, improved quality,

and reduced costs, solar cell manufacturers are

using machine vision systems to meet their goals.

The many manufacturing challenges are being

met by systems based on visible, near-infrared, or

electroluminescent imaging. Among their many

functions, these systems are inspecting for defects,

locating edges, and tracking and packaging solar

wafers through the production process.

This Vision Systems Design Editorial Digest

focuses on the ways in which machine vision can

aid the manufacture of solar cells, with articles on:

- challenges for automated optical inspection

systems throughout the solar cell production

process

- machine vision for edge isolation of solar cells

- inspecting and sorting screen-printed electrodes

- robotic systems for inspection and packaging

2 Uniquely Challenging 9 Machine

vision speeds edge isolation in solar cells

11 SolarScreening 18 Into

the Light

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3

Uniquely Challenging

Solar cell manufacturing faces several AOI challenges that can be met by visible, near-infrared, or electroluminescent imaging

By XInG-FEI HE

although thE EConoMiC situation slowed down

photovoltaic (PV) installation last year, the market

continues to grow. According to the European Photovoltaic Industry

Association (EPIA), the market will reach $31 billion with a capex of

$6 billion in 2010. With oversupply of capacity, solar cell manufacturers are under

pressure to find ways to increase efficiency, improve quality, and reduce costs.

Automated optical inspection (AOI) systems are growing rapidly as the solar

industry is reaching the stage of maturity in which machine vision undergoes

mass adoption on the production floor. A properly designed AOI can help meet

these goals. At some production stages, however, AOI system designers have

unique challenges to overcome.

Silicon-wafer-based solar cells currently represent 82% of the PV market, according

to EPIA. The typical solar cell has a structure (see Fig. 1) that needs a variety of

processing techniques to fabricate, including surface texturization and the silk-

screening of conductor layers for connection of the cell into a panel assembly.

Compared with traditional semiconductor electronics manufacturing processes,

wafer-based solar cell manufacture has some unique processing steps that

create both opportunities and challenges for AOI. While there is no standardized

production flow in the solar-cell manufacturing industry, a typical fabrication

process contains opportunities for AOI to aid in error detection and process

control (see Fig. 2).


4


An incoming wafer

inspection system looks

at the surface conditions

and geometry of the wafer,

checking for distortion,

chips, saw marks,

contamination, and obvious

cracks. It also looks for

micro-cracks, which are

especially problematic

for solar cell production

because they can result

in a wafer shattering

during the following steps.

Shattering not only results

in yield reduction but can

shut down the production

line for the time needed to

remove the debris.

Other AOI opportunities

include inspecting

texturization, antireflective

coatings, printed

metallization layers, and a

final operational inspection

of the finished cells that

takes advantage of the

solar cell’s electro-optical

characteristics.

Visible imaging

Conventional AOI systems

using visible light can handle a number of these inspection processes. Either

linescan or area-scan cameras can be used depending on the system design.

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Antire�ection layer

SiO2

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Wafer inspection• Surface and geometry• Micro-cracks• Warpage• Saw grooves and steps• Thickness• TTV

• Texturization control• Etching depth measurement

Cell-classification(FS and BS)

Print inspection (FS and BS)

Additional off-line equipment• Sheet resistance calibration• Contact and line resistance• Optical microscopes• Microbalances• Ellipsometer

• Sheet resistance measurement

Chemical etching

and texturization

Classification

and sorting

Co-firing Backside

printing

(full contact)

Backside

printing (pads)Frontsid

e

printing

Emitter

diffusion

p-glass removal

Silicon

nitride ARC

1 2

3

4

2

5

5

56

6

FIGURE 1. Silicon-wafer-based solar cell structures, typically sized at 156 × 156 mm or 210 × 210 mm with a thickness of 150–200 µm, include antireflection coating, surface texture that helps recapture reflected light, and frontside and backside metallization layers as electrodes.

FIGURE 2. A solar cell production line provides many opportunities for placement of AOI systems, some of which have requirements unique to solar cells. (Courtesy of ICOS, a division of KLA-Tencor)


5


In the geometry and surface inspection stages, for instance, visible light and a

monochrome linescan or TDI camera will suffice. These types of cameras allow

the inspection to occur in line, as the wafer moves along the production flow. The

size and type of defects for which the system is checking require cameras with

resolution in the range of 2k, 4k, or 8k pixels per line with pixels 7–14 µm in size.

The linescan or TDI rate needs to be fast enough to avoid creating a bottleneck

in the production flow—typically in the 20–100-kHz range depending on the

throughput. In addition, antiblooming or autoexposure features are desirable to

avoid image saturation.

Inspection of the antireflection coatings can also use visible light, but color

camera capability is required. Under white light, the antireflective coating on the

solar cell will appear blue because longer wavelengths transmit more efficiently

while the shorter wavelengths still experience reflection. The exact hue and color

saturation of the reflected light depends on the coating thickness.

Consequently, using the blue channel on a color linescan camera allows detection

of defects as well as process-control measurement. As with the wafer inspection

system, linescan cameras allow inspection of moving wafers. Color cameras with

1002VSDfea1F3b

Wafer travel (one per second)

Linear redLEDs

Linear redLEDs

Linearred

LEDs

DALSAPiranha 3 DALSA

Piranha 2

DALSAPiranha 2

DALSAEclipse

Resistivity/TTVtester(s)

Surfacecontamination

Micro-crack/saw cut

Geometry/chip-out

Linear NIRLEDs

FIGURE 3. The Meikle Automation IS360 Wafer Inspector (left) uses NIR backlighting to detect and measure micro-cracks. The system incorporates DALSA Piranha and Eclipse linescan cameras, which interface to National Instruments Camera Link and LVDS frame grabbers that are installed in high-speed image-processing PCs. The PCs run Meikle’s MASS material-handling and image-processing software. Standard encoder-based image acquisition along with continuous light and speed control ensures consistent image quality. Parallel processing is required to achieve one-wafer-per-second throughput (right).


6


4k- to 8k-pixel resolution and 20–40-kHz scan

rates are typical.

The metallization print process is another

opportunity for AOI to identify defects and

provide process-control measurements

using conventional cameras and techniques.

Two-camera systems can be used,

providing frontside and backside inspection

simultaneously.

near-infrared imaging using backlight

One of the most demanding AOI challenges is

during the incoming inspection stage: checking

for micro-cracks that can occur during

crystal growth and wafer sawing. This is a

critical checkpoint before beginning the cell

processing, both to ensure yield and to reduce

the occurrence of production downtime due to

a wafer shattering.

However, the micro-cracks can be too small—

less than 5 µm wide—to be seen during a

typical surface inspection. Furthermore,

cracks are difficult to detect using surface

illumination because there is almost no

contrast between the reflections of the crack

and the surrounding silicon.

Instead, this inspection requires the use of backlighting. Because the wafer is

only 150–200 µm thick and silicon is semitransparent in the near-infrared (NIR)

spectrum, LED backlighting using 850–950-nm wavelengths will provide enough

illumination to obtain useful images.

Under these backlighting conditions, a crack will scatter light and create a

dark line against a light background that is readily detectable. Sensors need a

FIGURE 4. Micro-cracks in polysilicon wafers can be captured using NIR backlighting and TDI cameras with sufficient sensitivity at those wavelengths. In the IS360 Wafer Inspector, pre- (left) and post-processed (right) images of micro-cracks are analyzed and the wafers sorted based on their dimensions using proprietary algorithms. (Photo courtesy of Meikle Automation)


7


resolution of 2–8k pixels with 7–14-µm pixel size to be able to detect these defects.

The challenge is that CCD image sensors lose quantum efficiency (QE) in NIR

wavelengths, resulting in a relatively weak signal. Moreover, camera systems

vary considerably in their NIR sensitivity. Some cameras exhibit as much as

30–40% QE at 900 nm while others can be much lower. Therefore, sensor and

camera selection are critical in developing an AOI system for backlit micro-crack

inspection.

System developers should also consider the exposure time needed to obtain

a usable image under NIR backlight because this directly affects the system’s

production throughput. The shorter the exposure time, the faster the production

line can run at this step.

Increasing the NIR backlight intensity is one option for reducing exposure time

but is limited by the availability of appropriate LED light sources. A more practical

approach is to utilize a time-delay integration (TDI) sensor. These sensors

utilize multiple TDI stages (as many as 100x) synchronized to the scan rate to

accumulate multiple exposures at each stage. The result is the equivalent of a

single line sensor with 100x of sensitivity. For example, DALSA’s Eclipse TDI has

a QE of 32% at 900 nm and 96 TDI stages, which provide a combined broadband

responsivity of 1950 DN/nj/cm2 at 0-dB gain.

Systems for performing backlight inspections have begun to appear. For example,

Meikle Automation developed the IS360 Wafer Inspector (see Fig. 3). As one of its

inspection steps, the IS360 detects and analyzes micro-cracks using proprietary

software and sorts wafers based on the dimension and area information of the

defects (see Fig. 4). These data also provide operators with feedback to improve

process control on the manufacturing floor.

Electroluminescence

Another AOI opportunity unique to solar cell manufacturing, along with

backlit micro-crack wafer inspection, is final quality inspection. This step takes

advantage of the solar cell’s electro-optical characteristics, which allow the cell to

generate luminescence for imaging.


8


This step can pinpoint micro-cracks and other production-induced defects in

the finished cell/panel that can cause early failure but might not be detectable

via conventional electrical testing. Eliminating defective cells at final inspection

can ensure that solar panels fabricated from the remaining cells have a product

lifetime exceeding 20 years.

The application of a current density of about 40 mA/cm2 to a cell causes

electroluminescence (EL) and results in the emission of light centered at 1.15 µm,

corresponding to silicon’s bandgap energy (1.1 eV). Any defects in the silicon will

appear by inspecting the uniformity of the EL (see Fig. 5). Photoluminescence (PL)

uses a similar method but with laser excitation rather than an electrical current.

The EL process has the additional attribute that the amount of light a cell

generates for a given applied current can serve as a measure of the solar cell’s

conversion efficiency. This means the final inspection step can not only detect

defects, it can help sort and grade finished cells by their output characteristics.

The results can also assist in process control as well as in matching cells for

compatibility in a solar panel assembly.

handling ir insensitivity

While AOI in the final inspection has the potential to provide significant

manufacturing benefits, it also represents a significant challenge for which there

is as yet no ideal solution. The problem is that conventional silicon imaging

sensors have extremely poor sensitivity beyond 1 µm, with a QE of almost zero.

As a result, the inspection requires long exposure times—on the order of

seconds—to obtain usable images. Right now, area-scan cameras are being used

in EL because of the long exposure time required and are creating a bottleneck in

production.

The image sensor industry is actively seeking a better alternative. One approach

is to use indium gallium arsenide (InGaAs) sensor technology, which has greater

sensitivity at 1.1 µm. Unfortunately, it is also expensive. Cameras using InGaAs

sensors cost from $20,000–$40,000, depending on the type of camera.

Also, the resolutions of currently available sensors—1k for linescan or 640 × 512


9


pixels for area-scan—are not high enough to meet the needs of these inspection

applications. Imaging an entire wafer with sufficient detail would require the use

of multiple cameras, multiplying the already high system cost.

An alternative is to use a fluorescent pigment coating on the silicon sensor.

Infrared photons striking the coating would generate shorter-wavelength

secondary photons, effectively converting the IR to visible light that the silicon

sensor can more readily detect. This conversion is known as an anti-Stokes

process and it has a low conversion efficiency

(0.2~2%), which results in limited improvement in

sensitivity requirements. The coating also could

reduce the sensor’s modulation transfer function,

thereby degrading sensor performance.

While the industry seeks to find a better solution to

EL/PL inspection challenges, the opportunities for

AOI in solar cell manufacturing remain. Many of the

inspection needs can be met with fairly conventional

AOI system designs and allow a tradeoff among

performance and cost to match different production line needs. To address solar

cell production’s unique AOI opportunities, however, developers should seek

effective ways of handling NIR/IR imaging.

XInG-FEI HE is senior product manager of linescan and TDI product lines at

DALSA, Waterloo, ON, Canada (www.dalsa.com).

DALSA,Waterloo, ON, Canadawww.dalsa.com

European Photovoltaic Industry AssociationBrussels, Belgium www.epia.org

ICOS, a division of KLA-Tencor Leuven, Belgium www.icos.be

Meikle Automation Kitchener, ON, Canada www.meiklesolar.com

Company Info

www.dalsa.com

www.dalsa.com

http://www.epia.org

http://www.icos.be

http://www.meiklesolar.com

10


Machine vision speeds edge isolation in solar cells

by AnDy WILSOn, Editor

MaChinE-Vision tEChnology is now being used in many stages

of photovoltaic production to supervise and control manufacturing

processes. “In edge isolation systems, wafer classification, and

solar module inspection, machine vision is being used to both

check the quality of individual

process steps and provide

feedback to better understand and

control manufacturing processes

themselves,” says Johannes

Stelter, head of the Intralogistics

and Image Processing Division at

Eckelmann (Wiesbaden, Germany;

www.eckelmann.de), a developer

of control systems for industrial

automation.

Integrated machine-vision systems

are required for the positioning

and measurement of geometric

properties such as edge and chamfer lengths of solar wafers. These systems must

also identify material faults such as microcracks or chips and guide laser-based

systems to provide laser edge isolation.

“Edge isolation provides electrical separation between the active front side of a

solar cell and the rear side,” says Stelter. “In the edge isolation process, a laser

cuts a small groove along the cell edges, the depth of the groove depending on

the width of the doped emitter layer of the cell. The difficulty lies in positioning

the groove as close as possible to the outer contour of the cell to maximize the

Eckelmann’s edge isolation system measures the outer contours of a solar cell and transfers the contour data to the control system of a laser that cuts a fine groove along the cell edges.

http://www.eckelmann.de

Machine vision speeds edge isolation in solar cells

11


active surface and thus the efficiency of the solar cell.”

To develop a laser edge isolation system for the Asys Group (Dornstadt, Germany;

www.asys.de), a manufacturer of handling systems and process machines for

the electronic and solar industries, Eckelmann teamed with Stemmer Imaging

(Puchheim, Germany; www.stemmer-imaging.de) to produce a system based

on linescan cameras that can measure the outer contours and transfer contour

data to the control system of a deflection mirror-based laser etching system (see

figure). Edge damage is detected and, if the damage is within tolerance levels, the

system proceeds with the cutting process.

To perform this task, images of each 125- or 156-mm square wafer is illuminated

with custom LED lighting and digitized with a 4k Piranha Color, 17-kHz linescan

camera from DALSA (Waterloo, ON, Canada; www.dalsa.com) fitted with a

Distagon 28-mm lens from Carl Zeiss (Oberkochen, Germany; www.zeiss.de).

Placed 300 mm above the solar cell to be inspected, the cameras is moved at 680

mm/s on a gantry, which results in a field of view of approximately 165 × 165

mm, producing images each of 4096 × 4096 pixels.

As the solar panel is moved across the camera’s field of view, images are

digitized into a PC using an X64 Xcelera-CL, also from DALSA, and edge

detection image analysis is performed. As the laser engraves the edge isolation

grid into the solar cell, grid distances are measured by the camera and any

differences in this value and the expected value are used to compensate for

nonlinear errors that may occur.

“Image acquisition and analysis take place in 800 ms,” says Stelter. The resolution

of the system makes it possible to ensure that laser edge isolation can be

performed less than 100 µm from the edge of the wafer.

Because the laser and camera system are automatically calibrated by the

machine-vision system, recalibration can be automatically performed after any

maintenance work. The system calculates a compensation map that is used for

error correction in the control system of the laser deflection unit. Errors in the

transport system of the machine can be also be diagnosed using the image-

processing system, thus allowing for remedial action to be taken.

http://www.asys.de

http://www.stemmer-imaging.de

http://www.dalsa.com

http://www.zeiss.de

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13


Solar Screening

Machine vision and robotics inspect and sort screen-printed electrodes, improve solar-cell manufacturing yields

By C.G. MASI, Contributing Editor

photoVoltaiC solar CElls start out as silicon wafers a fraction of

a millimeter thick. They are then trimmed to square or rectangular

shape a few inches across. The manufacturer also chamfers the corners

to reduce the possibility of

breakage. Subsequent processing steps

add several thin- and thick-film coatings

that give the cells the ability to convert

sunlight into electric power and channel

that power to a useful load.

Working with manufacturers of solar

cells, Adept Technology has been helping

automate several steps in the production

process. Recently, the company

developed a system to automate the

quality-control step between screen-

printing the final set of electrodes onto

cell surfaces and baking them to make

permanent metallic connections.

Before baking, any flawed patterns of the conductive ink can be washed off

and replaced, thus saving the wafers. After baking, such flawed wafers must be

discarded, wasting the cost of the silicon material and the value added by the

processing steps up to that point.

Robotic wafer-handling station uses grippers such as suction cups (shown) or those based on the Bernoulli principle to lift and sort solar wafers after inspection.

Solar Screening

14


automated inspection

The machine-vision-based automated inspection system and the robotic wafer

handler sit between the screen-printing station and bake oven on the electrode-

printing production line (see Fig. 1). This is the next-to-last step in the production

process, and wafers have already gone through several thin-film coating

procedures to make them convert light to electricity efficiently.

The final step, adding

the electrodes, is

needed to channel

electricity out of the

wafer. Once wafers

leave the oven, they

need only a clear

conformal coating to

protect active layers

Adept Technology found that the existing production process at a solar cell

manufacturer was bogged down by reliance on manual inspection to ensure

the quality of screen-printed electrodes before they were permanently baked

onto silicon wafer surfaces. “With several different people looking at wafers

from shift to shift,” Todd Reynolds, applications engineer at Adept, says, “one

person rejects more cells that should pass, and others pass more cells that

should have been rejected.

“Combining machine vision and robotics allows for less waste,” he

adds. “We’re able to look for things like length and width of wafer.

That allows us to get a refined center for the wafer for handling

with robots. We can find chips and cracks on the wafer and

inspect the printed lines for a variety of different defects.”

Improving throughput and yield near the end of the production

process will have a major impact on total manufacturing cost.

Reject bins

Screenprintingstation

Automatedinspection

station

Wafer-handling

robot

A B C D

Oven

Toconformal

coatingstation

0907VSDfea2profilF1FIGURE 1. Combining machine vision and robotics for automated inspection before baking electrodes onto wafers improves process yield.

Solar Screening

15


from environmental effects before shipping to customers. Wafer losses at this

point are maximally expensive, so high yield is critical.

Printing is a silk-screening process that lays down a pattern of conductive-ink

electrodes. The ink consists of fine metal particles suspended in a two-part fluid

consisting of a volatile solvent and an organic binder. After the screen pattern

has been laid down, the solvent rapidly

evaporates, leaving the metal particles

held in place by the binder.

This is the stage at which it pays to

inspect the pattern and recycle wafers

with printing flaws. At the same time,

cracks, contamination, and any other

flaws in the wafers are investigated.

The inspection unit mounts into a light

shield shaped like a truncated pyramid

mounted over the conveyor line

carrying wafers (see Fig. 2). The camera

mounts in the upper portion, and a row

of LED lights on either side of the conveyor illuminates wafers as they come by.

The conveyor runs at a constant speed, with the LEDs being strobed to stop the

motion.

At 26-in. high and covering a 12-in.2 footprint, the inspection unit is designed

to mount 1 in. above the conveyor. This allows the light shield to exclude most

ambient light while allowing wafers to move freely on the conveyor. The LED light

lines are positioned so that specular reflections from the wafer surfaces miss the

camera aperture and are absorbed by the light shield’s inner surface.

The system uses a Basler piA2400-12gm Gigabit Ethernet camera connected to

Adept’s proprietary Smart-Vision EX vision controller. The unit is a PC-based

vision controller running the company’s Automation Control Environment (ACE)

and AdeptSight machine-vision software.

LEDField ofview

Camera

Wafer

Lightshield

Reflectionsmiss camera

FOV

Lens

Conveyor

LED

0907VSDfea2profilF2FIGURE 2. Placing lines of LEDs within a light shield illuminates conductive ink traces while avoiding specular reflections.

Solar Screening

16


ACE is an integrated, point-and-click development environment with

configuration tools and basic programming features for robotics applications.

AdeptSight includes utilities for vision-guided and inspection applications.

The twin requirements of automated inspection and robot guidance call for

different analysis algorithms applied to the same image. As the wafer moves

on the conveyor, the

first step is to acquire

an image from the

camera and upload

it over the GigE

connection to the

vision controller for

processing.

Thresholding and

edge finding bring out

the wafer outline as

well as highlighting

the screen-printed

electrodes. Inspection

tasks are broken into

three areas: print inspection, inspection for chips, and pattern recognition. Print

inspection ensures the electrode edges are crisp, form the correct pattern, and

are properly registered with the wafer edges (see Fig. 3).

Inspection for chips ensures that the wafer outline does not vary from the correct

size and shape: All edges must be straight; dimensions must be correct; and the

corner chamfers must also be correct. Cracks appear as extraneous irregular

lines that can be highlighted by enhanced contrast and thresholding. Pattern-

recognition algorithms search for anything out of the ordinary such as cracks or

broken ink lines. Linear measurement algorithms verify correct placement of ink

patterns that are supposed to be there.

Wafer handling

The wafer-handling station following the inspection uses an Adept Quattro

Figure 3. The machine-vision software locates a wafer with a refined center. Both busbars have been located and inspected for width, length, and defects (e.g., skips in the printing, bulges, necking).

Solar Screening

17


robot with an unusual robotic gripper and coordinates with the vision system to

automate both the inspection and handling of the wafers. The Quattro robot is a

variation of the fastest type of robot available: the delta robot.

The older delta robot design has three “arms” attached to a “base” mounted over

the work space. Each arm consists of an upper member—which is driven by a

servomotor built into the base—and a lower member attached to a platform that

carries a gripper (or whatever end effector the application calls for).

Passive joints at the lower member’s ends allow the arms to flex in a coordinated

way under the influence of the separately controlled servomotors. A rod driven

by a fourth servomotor rotates the gripper as needed, providing the robot with a

fourth degree of freedom.

Calculating servomotor rotations to effect a required motion, however, is more

complicated for a delta-robot controller than for, say, a conventional six-axis

selective compliant articulated robot arm (SCARA) because the delta robot’s

control degrees of freedom do not match any spatial

coordinate system. To make a motion along any given axis

requires coordinated action of all servomotors.

The Quattro robot differs from delta designs by having a

fourth arm and no central rod. The rotational degree of

Platform(springs not shown)

Outer arms

Ball joints(springs not shown)

Motorcover

Base

AIBCable cover

(IP-66 option)

Innerarms

Mountingpads

0907VSDfea2profilF3b

FIGURE 4. Adept’s Quattro robot is a modification of the delta robot configuration. Unlike delta robots, the Quattro robot rotates its platform by shifting

“shoulders” in the base.

Solar Screening

18


freedom is made possible by providing “shoulders” in the base that move the robot’s

four arms relative to each other. This arrangement makes it possible to increase

the Quattro robot’s strength, speed, and agility relative to delta designs, but it also

further complicates the motion controller’s kinematic calculations (see Fig. 4).

In another departure from conventional material-handling robot practice, rather

than use a more traditional suction-gripper technology to pick up and move

wafers, Adept’s design uses a gentler method based on Bernoulli’s principle.

Suction grippers use vacuum to pull the wafer against an elastomeric cup.

Friction between the cup lip and the wafer then provides the force needed to

move the wafer.

Bernoulli’s principle, on the other hand, shows how to use positive air pressure to

pull the wafer close to, but not in contact with, a flat plate. The principle says that

along a streamline, air pressure is inversely proportional to air speed squared.

Air forced through a narrow gap between the gripper plate and wafer must move

rapidly to escape. Once outside the gap, the air effectively slows to a stop. Thus

air pressure in the gap must be much lower than atmospheric. Ambient pressure

will press the wafer

close to, but not in

contact with, the

plate. The wafer

cannot make contact

with the plate, since

that would block the

airflow and remove

the effect.

ServomotorsInverters

Motion controller

Smart-Vision EXvision controller

Strobecontroller

Camera

Quattrorobot

LEDs

0907VSDfea2profilF5

FIGURE 5. The vision controller sends information via FireWire about part locations and motion control to a motion controller and multiaxis servomotor drive housed in the robot’s base.

Solar Screening

19


Suction grippers create a concentrated stress around the suction inlet port,

whereas the Bernoulli gripper spreads the stress over the whole gripper plate.

This phenomenon greatly reduces the maximum stress level in the wafer and

subsequent breakage.

robot guidance

Robot guidance needs are fairly simple. The robot needs to know exactly where

the wafer center is on the conveyor and what its orientation is so that it can

correctly pick up any wafer that needs to be moved and avoid collisions during

movement. The vision controller sends information via FireWire about part

locations and motion control to a motion controller and multiaxis servomotor

drive housed in the robot’s base (see Fig. 5).

Observing the angles that edges make with the image frame provides orientation

information. The controller can then transform to a rotated coordinate system

aligned with the wafer edges. This also helps the wafer inspection task. Finally,

finding the wafer center is a matter of averaging the coordinates of opposite edges

and rotating the results back to the conveyor-based coordinate system.

It would be possible to determine the conveyor speed

by calculating wafer-center locations in two subsequent

images, but it is better to rely on sensors built into

the conveyor system. A vision-based measurement

only provides one average speed measurement during

a particular time interval and cannot capture slight

variations in conveyor speed that might affect positioning

by the time a wafer reaches the robot position. Conveyor speed sensors, on

the other hand, can provide constant real-time monitoring for more accurate

positioning.

For wafers that do not pass inspection, the vision controller has to decide

into which of four reject bins they should go: those that can be cleaned and

reprocessed, and those with different kinds of uncorrectable defects, such

as corner chips, edge chips, and cracks. Since different defects arise from

different causes, separating according to defect type helps continuous process

improvement efforts.

Adept Technology Pleasanton, CA, USAwww.adept.com

Basler Vision ComponentsAhrensburg, Germany www.baslerweb.com

Company Info

www.adept.com

www.baslerweb.com

20


Into the Light

Vision-guided robotic systems inspect and package solar cell wafers

by JOyCE LAIRD, Contributing Editor

EVErgrEEn solar produCEs solar panels using its proprietary

crystalline silicon technology known as String Ribbon. To manufacture

the wafers that form the basis for the panels of solar cells, parallel wires

or strings are pulled through a silicon melt and the molten silicon spans

and solidifies between the strings, creating a long ribbon that is periodically

harvested for processing into solar cells. This unusual method of developing solar

cells has proved to be simple, efficient, and commercially successful.

Success has required greater

processing speed to meet demand.

The original gantry fixed automation

system developed in-house to handle

the inspection, sorting, and packing

steps was becoming too costly to

maintain and required five pickup

steps to move the pre-etched silicon

wafers to their final destination after

they had been acid-etched to clean

the surfaces.

Peter Kane, principal engineer at

Evergreen, says that the less the wafers

are handled the better. “We decided

that going to a robotic system could

bring this handling down to only two

FIGURE 1. Robots pick wafers for inspection in the Evergreen Solar pre-etch inspection, sorting, and packaging machine.

Into the Light

21


steps. We selected Adept robots and built a system around the robots’ capabilities.”

The plan encompassed two separate applications: front and back end, each

requiring their own specific process steps. Kane was responsible for the design

and construction of the overall system, including automation of the cell area.

Glenn Bohling, also a principal engineer, was responsible for the controls and

software development.

Kane designed a system that would handle both sides of the process. On the

front end, two robots pick up wafers for inspection, sorting, and loading onto a

conveyor, and on the back end two robots unload the wafers from the conveyor

and put them into boxes. Solar cell wafers require precise handling and final

inspection for a variety of faults before they are ready for further processing.

“We designed everything for the system we wanted in-house,” Kane says. “There

were two types of vision integration needed. The back-end machine had to pick

the wafers up off of a moving belt and Adept offered a well integrated vision and

motion package.”

“We use the packaged system: AdeptVision sAVI

vision, conveyor tracking, and the robot all

integrated,” says Peter Kane, principal engineer at

Evergreen Solar. “We did that because they offer

an off-the-shelf system specifically for this type of

application. Since all the engineering was basically

done, it made a lot of sense to use a vision system

designed for picking parts off of a moving conveyor.”

He adds, “The conveyor tracking side would have been

very difficult for us to do from scratch and would have

required a huge amount of development work. Installing

the system after it was assembled was the easiest part

of the process. We increased our solar cell yield about 1%

because of less wafer breakage and greatly increased uptime.”

Into the Light

22


The robotic system uses the Adept Cobra s800, a SCARA-type robot, and includes

the Adept SmartServo controls architecture that supports options such as

conveyor tracking and vision-guided motion. The diagnostics display enables

troubleshooting, and the robot has integrated temperature sensors to monitor

heat in servo motors to prevent damage.

system design

On the front end, the system is required to pick wafers out of boxes and move

them to a camera station to inspect the wafers for imperfections such as

cosmetic defects, edge defects, and cracks. It also needed to determine x-y and

theta position of the wafers when placing them on the conveyor (see Fig. 1).

“We use the ePLC version of the 800 robot for this,” Kane says. “An Allen Bradley PLC

controls each robot and it also talks to the vision system. We use two Cognex DVT

540 smart cameras, which include a processor, image sensor, the optics, and light.”

The Adept ePLC Connect Server software provides connectivity with the PLC.

All application programs and locations are defined and reside within the Allen

Bradley PLC. The PLC client retrieves this data and commands the robot to move.

The robot lifts an individual wafer from a box, moves it over the camera, which is

located below the conveyor; LED lights on the robot provide additional backlighting

for the camera. The wafers are visually inspected; rejects are set aside. The

software records the processing data for statistical process control (SPC) recall and

evaluation. Reasons for rejects are noted. If they pass inspection, wafers are placed

on the conveyor to be moved through the acid wet bench (see Fig. 2).

0810VSDfea3F2

Product flow

Productflow

LoaderAdept ePLC Cobras with LED lighting

and two Cognex DVT cameras

Wet bench

UnloaderAdept dual s800 Cobras withfluorescent backlighting and

conveyor tracking with vision

FIGURE 2. To inspect solar cell wafers, two robots unload boxes, place wafers on a con veyor after inspection by a smart camera for sorting and acid wash, and then load them into boxes after a final inspection.

Into the Light

23


Kane says that Evergreen has

used the DVT cameras for

many other machine-vision

applications within the plant,

so the company is familiar

with the benefits of these

smart cameras. The Cognex

DVT system uses a Hitachi

SH4 processor and has 64

Mbytes of RAM and 16 Mbytes

of flash memory. The CCD

camera operates at 75 frames/s

and supports partial frame

acquisition.

The smart camera

communicates through

Ethernet and RS-232. Vision tool

support includes identification

(modeling), readers (ID, 2-D,

OCR/OCV), flaw detection, and

application-specific scripting.

The camera can analyze for odd

shapes and inspect for multiple

and overlapping parts in a

range of light conditions, which

means it is suitable for robotic

guidance.

“We use the Allen Bradley ControlLogix PLC platform and DVT vision with

standard lenses; there is nothing really proprietary here,” Bohling says. “We use

a state diagram system. All the PLC programming is based on state diagrams,

which detail the logic flow of the machine sequences.

“We use custom Visual Basic operator interface applications,” he adds. “We

developed these in-house. It’s standard on all of our machines so that any

FIGURE 3. The operator interface is a customized GUI that is standard on all plant equipment.

FIGURE 4. At the pre-etch unloader end, vision-guided Cobra dual s800 robots pick the wafers from the conveyor and load them into boxes.

Into the Light

24


operator can go to any machine in the plant and

know how to use it.” (See Fig. 3).

Front and back

On the out-feed end, a Dorner conveyor was used,

incorporating simple fluorescent backlighting.

The wafers pass over a backlit area so the out-

feed robots’ integrated vision systems can capture

a binary image of a black wafer against a white

background, inspecting for cracks and chips.

The unloader is a fully equipped Adept Cobra dual

s800, which uses a single controller to control both

robots with sAVI Vision and conveyor tracking. It coordinates robotic movement

with wafers being presented on a moving conveyor (see Fig. 4). Each unloader

robot incorporates an RS-170 camera to inspect the wafers. Any wafer that fails is

not picked up and instead drops off the end of the conveyor into a reject bin.

The entire robotic integrated system for loading/inspection and unloading/

packing was built at the Evergreen plant offline. It took one day to dismantle and

move the old gantry system out and install the new system.

Evergreen currently has 10 Adept robot systems running at its plant in

Marlborough, MA. The downtime for maintenance has been essentially

eliminated in comparison to the old gantry-based automation system that was

down for maintenance at least once a week. The conveyors, PLC controllers, and

cameras had all been in use on other systems within the plant, and proved robust

and reliable when integrated with the robots.

Adept Technology Livermore, CA, USA www.adept.com

Cognex, Natick, MA, USAwww.cognex.com

Dorner Manufacturing Hartland, WI, USA www.dornerconveyors.com

Evergreen Solar Marlborough, MA, USA www.evergreensolar.com

Rockwell Automation Allen Bradley Control Systems Milwaukee, WI, USA www.ab.com

Company Info

www.adept.com

www.cognex.com

www.dornerconveyors.com

www.evergreensolar.com

www.ab.com

25


DALSA Corporation (www.dalsa.com) is an international technology leader in the design, development, and manufacture of digital imaging products and solutions. In addition, DALSA specializes in the engineering and fabrication of semiconductor components and services. The company has grown and currently employs approximately 1000 people world-wide with sales offices across North America as well as in Europe and Asia supporting an international distribution network serving more than 40 countries. Today, DALSA image sensors, cameras, frame grabbers and software are used in thousands of automated inspection systems around the world and across multiple industries including semiconductor, flat panel display, electronics, and manufacturing.

links:

DALSA capabilities in Aerospace and Defense

The Power of Next Generation GigE Vision v1.2

Get More Vision. DALSA’s 2010 Machine Vision brochure.

New HS Interface: Next Generation Camera-to-frame grabber Interface.

DALSA Industrial Vision Solutions brochure.

http://www.dalsa.com/

www.dalsa.com

http://www.dalsa.com/sensors/applications/aerospace.aspx

http://www.dalsa.com/editorialdigest/gige/v10

http://www.dalsa.com/editorialdigest/2010mvb/v10

http://www.dalsa.com/mv/knowledge/hslink.aspx

http://www.dalsa.com/editorialdigest/ipd/v10

editorial digest - vision-systems.com · editorial digest - vision-systems.com

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