Photonic Device Placement
High-resolution imaging demands a combination of custom and off-the-shelf hardware and software.
Automating photonic device assembly processes can seem daunting. In a typical application,
progressive assembly operations such as locating, guiding, gluing and bonding take
place in a local region around a static package. The challenge is to integrate these
different mechanical systems precisely to fit all active components within a small
Consider the basic task of inserting an optical
fiber into a ferrule for gluing. Prior to insertion, the automation system must
determine the exact location of the fiber end in three-dimensional space while faced
with positioning error related to limited repeatability in gripping and to fiber
Figure 1. The inspection strategy for this assembly operation for a transimpedance receiver combines air bearing servos, vision guidance and active vibration damping on the Z-axis.
To accurately pinpoint location, the
vision system often must access multiple views and magnifications of the target
object (Figure 1). It also may be necessary to maximize the optical working distance
to allow assembly directly beneath the camera. Searching for system resolution,
end users will most likely find that their application requires a combination of
off-the-shelf and custom vision components and software.
Cameras and lenses
Precision vision applications require camera lenses
with high magnifications. Such optics not only have shallow depths of field and
short working distances, but also require bright light. The magnification of the
lens won’t limit system resolution, but its quality and the wavelength of
the illuminating light will. High-quality, high-magnification lenses have a high
numerical aperture. In practice, a system featuring 103 magnification, a numerical
aperture of 0.28 and 640-nm light offers resolution of 2.78 μm, but this would
increase at shorter wavelengths (Figure 2).
Figure 2. For a vision system to resolve black objects, there must be some area with a different gray level between them. To define the smallest distance (d min) separating
the two black objects, d min = 1.22 x λ/NA. Both d min and λ are in microns.
Contributing to the quest for resolution
is the camera. The camera’s photosensor captures light and assigns a gray
level to each pixel. Row by row, the system reads pixel intensity values from the
sensor and sends them to the image processing system. The sooner the system converts
sensor information to a digital signal, the better chance it has of preserving true
In an analog system, the image processor
resident on the computer bus digitizes the image. Noise from the bus and peripheral
components can degrade gray-level resolution from 8 bits to 5 or 6 bits. Uncertainties
in the pixel clock also create inaccuracies in how the system assigns a gray level
to a pixel (jitter) and how gray levels of one row of pixels relate to the levels
of the next row.
There are no such problems in a digital
system. The analog-to-digital conversion takes place inside the camera, and the
resident pixel clock is digital. Resolution is 10 bits or more.
Specifying a digital system does not
guarantee the best vision system, though. Digital cameras with CMOS imagers have
some limitations because the imagers are pixel-addressable, and the conversion electronics
are adjacent to the photo site. The spacing between sites is greater than that of
a CCD imager. Therefore, for a given area, the camera is less sensitive to light.
In addition, CMOS imagers are inherently noisier because more electronics are near
the analog-to-digital conversion area.
Whether digital or analog, the pixel
size is the driver for effective system resolution. Using a 10x magnification with
a 7-μm pixel yields a theoretical resolution of 0.7 μm. Typically, the
smaller sensor formats 1/4 and 1/3 in.) have a smaller pixel size, which means that high-magnification applications are generally addressed with these formats. This reduces system magnification and increases depth of field and lighting uniformity across the field.
Standard cameras are the most sensitive
to light at a wavelength of 600 nm and have 70 percent less sensitivity to 400-
and 800-nm light. Cameras sensitive to UV radiation are available at higher cost.
Shedding light on lighting
For most vision applications, backlighting is
the technique of choice. Unfortunately, because of space constraints or the need
to work inside a package, this technique is rarely usable in photonic device assembly.
The other option usually involves one of two common top-lighting methods.
Figure 3. Coaxial lighting works well when illuminating reflective
or uneven surfaces perpendicular to the camera axis. This 125-μm fiber is illuminated
with a white background (left) and without (right).
• Through-the-lens coaxial
light integrated into the optics. This works well when illuminating reflective
or uneven surfaces perpendicular to the camera axis. The illuminated area is equal
to the primary lens’s viewable field. Because the concentrated light fits
within the field, it provides the most power (Figure 3).
#8226; Top lighting with a line
light or ringlight. This technique provides uniform and shadow-free images with
the light centered on the object and at the proper working distance and angle (Figure
Figure 4. Top lighting provides uniform and shadow-free images with light centered on the object and at a proper working distance and angle. The 125-μm fiber is illuminated with a white background (left) and without (right).
In both instances, the light source
involves either LEDs or a halogen lamp. Output intensity limits the useful working
distance of an LED ring. Minimize working distance, however, and the coaxial technique
using an LED will work for some higher-magnification applications.
Illuminating the object using a halogen
lamp and a fiber bundle is the most common way to obtain the intense light required
by the application. Fiber bundles are bulky and restraining, however, and they may
cause unwanted motion errors when tethered to a camera lens or moving stage.
Halogen lamps also have drawbacks.
At full power, a lamp may last only 400 hours. As lamp life dwindles, so does its
output, affecting the image quality. More costly lamp houses can compensate for
the intensity decay by monitoring the output and increasing the current available
to the lamp.
To reduce distortions from nonuniform
bending of light traveling at different wavelengths through multiple lenses, systems
should use a single-color light source when available. Options include single-color
LEDs or halogen lights with a bandpass filter added to the output. Assuming the
system uses standard cameras, red light is the most efficient option because most
cameras are optimized for that color. Shorter wavelengths will produce the most
resolute image but require greater intensity because of reduced camera sensitivity.
High-power spectral line lamps and lamp houses are available for intense wavelength-specific
illumination at shorter wavelengths.
Most vision applications for precision photonic
device placement require Z focusing to compensate for planar differences of parts,
nests and carriers. A lens has an optimum working distance. High-magnification optics
have a shallow focus range. Z focusing is the act of moving the camera relative
to the object to bring it into the focus plane.
Pick-height repeatability may be necessary
for consistent picking of a delicate object or the precision placement of a device
in Z relative to another device. Z focusing establishes a repeatable height relationship
between the focal point of the camera and the object to be picked. Once the object
is precisely located, the mechanism moves to the object using an offset in X-Y-Z.
The focusing can be achieved in two
ways. The optical system may come with an internal motorized focusing element, or
the user can vary the distance between the camera and lens and the imaged object
by moving either the camera or the part. In theory, the software approach is straightforward.
Typically, the system works with a set edge location within a region of interest,
taking images as the mechanics move through a focusing range. A gradient operator
on each image determines the contrast of the edge to the background. Software optimization
leads mechanics to converge on the best edge result.
Many factors influence how a system
converges to an in-focus condition. For example, resolution, settling time and speed
are important considerations for the motion system responsible for the focusing.
A lens with a longer focal depth will allow larger step sizes, thereby reducing
the number of iterations necessary at the expense of resolution. The acquisition
time of a standard analog camera is 33 ms. Digital cameras are at least twice as
The last consideration is the vision
processing time. Processing boards with resident processors operate more quickly
than those that rely on the computer for processing. Tightly integrating the motion
and vision for on-the-fly acquisition can substantially reduce the time between
reiterations. End users should consider five images per second a worst-case Z-focus
There is one requisite for this software
approach to work, though. The system must find an edge. For the vision sensor to
place a region of interest around an edge, it must be in a repeatable area in the
field of view. The system could search for the initial position of the edge within
the field, but it must first be at or near focus. This creates a chicken-and-egg
scenario. The edge cannot be found unless it is in focus, and the focus cannot be
discerned unless an edge can be found.
When the system does not know the precise position
of a feature or object, dual magnification with Z focusing is useful. Systems integrators
can develop a routine to coarsely locate a target feature at low magnification and
then precisely find a datum at high magnification in three-dimensional space.
The setup can include separate cameras,
each with fixed magnification, in a high/low magnification scenario. This requires
a 3-D calibration routine to relate the two. Either the cameras or the object to
be inspected must move to obtain the high- and low-magnification images. When the
application demands a precise mechanical relationship between the two images, engineers
also must consider motion-system repeatability and accuracy.
Another dual-magnification technique
employs a custom-designed optical assembly with a common input lens to split optical
paths to two cameras. There are separate magnifications, as well as different optics
in each path. With the relationship between the cameras mechanically fixed, the
software can develop that relationship precisely. Lighting required for high magnification
is greater than for low magnification, so programmable lighting intensity or blocking
filters must be added to the optical path to obtain the same illumination levels
under different magnifications. Because depth of focus will be greater at lower
magnification, the whole assembly may have to move in the Z direction relative to
the imaged object when switching between magnifications.
A motorized turret can provide a way
to move lenses with different magnifications in front of the camera, but the mechanics
of obtaining constant lighting and consistent alignment are difficult to implement.
Motorized zoom-and-focus lens assemblies with high optical quality also are available.
They offer configuration and working-distance flexibility, but can take several
seconds to change from high to low magnification and are not necessarily durable.
They also won’t work well on high-throughput machines.
Although there are several ways to
accomplish a dual-magnification scheme, it is important to consider the application’s
lighting and cable management issues before selecting a technique.
Hardware and software
The vision system should support multiple standard
(640 x 480) and large-format (1k x 1k) cameras. Some applications can require four
or more cameras. For accuracy with an analog approach, the pixel jitter specification
of the acquisition board should be below ±2 ns. End users working with a digital
path, on the other hand, should note that there is no standard interface format.
They should make sure that the system supports RS-422 or EIA-644 formats and one
of the newer Camera Link or IEEE-1394 cameras.
The setup also requires robust and
repeatable image analysis tools to make the best use of the image. No matter how
good the contrast is between an object and the background, a highly magnified object
will still look fuzzy and will have edge transitions that fall across many pixels.
The challenge for vision algorithms is to determine repeatedly where the edge transition
Geometric-based searching goes one
step further. By developing relationships between multiple edges of an object, this
search tool learns only these relationships and does not consider the gray level
or texture of the object or the background. This technique is fast and can find
objects with major differences in lighting, scale and rotation.
Shape-based edge-finding tools add
important capabilities. They accurately locate roughly defined edge-based features
like lines, pairs of lines, curves and circles, summing gray-scale image data along
one coordinate of a region of interest to create a one-dimensional projection and
then extracting edge data from the differentiated projection.
Cost vs. accuracy
Also helpful is the capability to build an internal
look-up table to seamlessly compensate for linearity and perspective errors of cameras.
Any nonlinear distortion in the optics will cause pixel-to-real-world unit calibration
errors. Nonperpendicularities between the camera optics and nesting plates also
can cause perspective errors.
Ultimately, developers of precision
systems for photonic device placement face some trade-offs between cost and accuracy.
No matter how well engineered, manufactured and assembled, the device will have
inherent mechanical inaccuracies. End users can significantly reduce system errors,
though, using the machine vision system, low thermal expansion grid plates and mapping
Meet the author
Bruce Fiala is a senior software engineer for
robotics and vision at RTS Wright Industries LLC in Nashville, Tenn.
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