Production Necessity

Jun Mitsudo describes advances in semiconductor inspection enabled by machine-vision algorithms, sensors, and processors

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Jun Mitsudo describes advances in semiconductor inspection enabled by machine-vision algorithms, sensors, and processors

VSD: What is the mission of Canon Machinery in designing and building machine-vision systems for end users? Which industries do you serve?

Mitsudo: Canon Machinery consists of two business divisions: one that develops machines for factory automation and another that builds die-bonding machines for semiconductor test and assembly. Canon is the largest manufacturer of these machines in Japan and fourth worldwide.

Being committed to investment in research and development in semiconductor production technology, we realize that machine-vision technology is a necessity. Indeed, because semiconductor production equipment is always increasing in complexity, the number of cameras required per machine is becoming larger each year. These cameras are used in a number of automated machine-vision processes including high-accuracy alignment, part recognition, part identification, and optical character recognition (OCR) applications.

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Canon Bestem-D02 is a multipurpose die bonder with a bonding speed of 0.29 s/cycle. The bonder incorporates CMOS image sensors with programmable ROI imaging. Image analysis is performed using Halcon from MVTec and a library customized by Canon Machinery.
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VSD: What are end users requiring from Canon Machinery in the design of new systems?

Mitsudo: In factory automation systems, many different features are required that can only be produced at a reasonable cost by closely collaborating with end users. However, in the development of automated die-bonding equipment, the most important criterion is the throughput of the system. To achieve the highest possible throughput, many different technical factors such as speed, accuracy, and robustness need to be considered.

In addition, machine operators must configure these systems as quickly as possible. This is especially important since semiconductor manufacturing is now being performed in developing countries, where an easy-to-use operator interface is critical to the manufacturer’s success. In future, these sophisticated operator interfaces will take advantage of different types of sensing technologies including machine vision to detect the status of a system and inform the operator accordingly.

VSD: What technologies and components do you use in these applications?

Mitsudo: Depending on the type of application, the best fitting components that address the different requested features and specifications of each machine are individually chosen on a case-by-case basis. Because semiconductor devices differ in size, die-bonding machines are required to accommodate many different types. Indeed, the smaller the size of the device, the greater the required throughput of the system.

For this reason, CMOS cameras with programmable regions of interest (ROIs) are especially useful since these ROIs can be dynamically changed depending on the size of the individual IC. These types of cameras also eliminate the necessity to use relatively expensive zoom lenses.

To perform image analysis, we use Halcon from MVTec Software (Munich, Germany; and create our own features based on the library. In the past, we developed our own image-processing hardware or bought off-the-shelf image-processing boards. However, in late 1990, the processing power of the PC increased dramatically and after an extensive evaluation we selected Halcon as our software package of choice.

VSD: What developments in embedded computing, GPUs, multicore CPUs, and multicore DSPs do you see? How will these technologies affect hardware development and how will system designers incorporate these developments?

Mitsudo: Of the different types of hardware currently available, perhaps graphics processing units (GPUs) are the most important. The high level of data parallelism used in these devices makes them an interesting alternative to general-purpose CPUs, especially in image-processing applications where very large images must be processed at high speeds.

For this to occur, however, system designers must have an intimate knowledge of computer architectures, algorithms, signal processing, optics, and mechanical design. In current die-bonding applications, newer algorithms are required to replace gray value edge-based template matching, and we expect such algorithms to be ported to GPU-based machines to increase their speed.

VSD: What algorithms and specific software developments do you see emerging in the next five years?

Mitsudo: Different algorithms for 3-D pose calculation and 3-D shape reconstruction must become easier to integrate and maintain. Although these technologies are already practical, their use is limited due to limited acceptance by system designers. In the future, however, sophisticated software interfaces will make such software much easier to use.

VSD: What could vision component manufacturers do to make your job easier?

Mitsudo: In industrial machine-vision systems, the introduction of high-end machine-vision tools for template matching, caliper measurement, and blob analysis has made the development of die-bonding machines much easier. As these features migrate to smart vision sensors, they will become more practical and more widely used on the factory floor.

Other functions such as the fast Fourier transform (FFT), feature point extraction, calibration tools, neural network, and support vector machines (SVMs) are also being incorporated into many off-the-shelf software packages. As system designers, we are committed to providing end users with the best solutions by combining these elemental technologies.

For this, we must test the feasibility of use of each function and this requires an enormous amount of time. Single software packages that incorporate all of these functions therefore prove most valuable.

Because we incorporate ROI processing of CMOS cameras, we can dynamically change image-acquisition parameters to search for any specialized ROI within the image. Because this requires sending commands to the cameras continuously, standard digital interfaces such as Camera Link, FireWire, or GigE are useful in easing the setup of these types of cameras in semiconductor inspection applications.

VSD: In which industries do you see the most growth? In which geographic areas?

Mitsudo: Alternative energy sources have found increased popularity, especially after the price of oil increased to over $140 per barrel. We see this trend continuing with developers looking to produce automated systems for the inspection of solar wafers, solar cells, solar panels, and compact rechargeable batteries.

VSD: What kinds of new applications for machine vision do you expect to emerge? What new software, components, and subsystems will be needed?

Mitsudo: Although many newer machine image-processing algorithms offer high potential, they typically cannot overcome the cost and speed requirements of die-bonding applications. However, looking at future innovations in systems based on DSPs, GPUs, multiple CPUs, or FPGAs, it is likely that such algorithms may soon become practical.

In future, we hope to deploy systems that automatically detect the multiple processing resources available on a system and combine them efficiently for different processing tasks. These systems may perform functions such as point processing and neighborhood operations on an FPGA and perform other functions using a distributed computing system consisting of multiple GPUs or multicore CPUs. From a user’s perspective, the use of this hardware must be transparent.

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Jun Mitsudo holds a PhD in 3-D shape measurement from Ritsumeikan University (Kyoto, Japan) and is currently assistant manager of the Research and Development Center of Canon Machinery (Kusatsu, Japan). He has been involved with machine-vision technology since the late 1990s.

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