Application Story

Refining Defect Detection Algorithms
for More Accurate, Effective PCB
Quality Inspection
Industry: Industry 4.0
Product: MAXER-2100

Introduction
The effectiveness of printed circuit board (PCB) quality inspection solutions can be broken down
to three facets - automated Optical Inspection (AOI) systems that are used to acquire images of
PCB components, inferencing software used to detect defects, and hardware connecting the two.
The latter of these is responsible for processing data obtained from the AOI system and runningdefect detection models, typically via its GPU, to perform key tasks within the quality inspection
solution as a whole.
This is why one vendor chose to incorporate AAEON’s new MAXER-2100 as an edge server whenbuilding their quality inspection system, the Hawkeye. The company developed cutting-edge
software to identify defects in PCBs. They then deployed the MAXER-2100 to process image datafrom an AOI device and run their AI inferencing models via the MAXER-2100’s GPUs to quickly
and accurately detect defects during the manufacturing process.
This comprehensive quality inspection process instilled such confidence that AAEON
consequently chose to deploy it in its own factories, using an AOI device to obtain data and theMAXER-2100 to run the vendor’s defect detection models to determine whether various
components passed or failed. This reduced AAEON’s reliance on manual inspection and increased
the accuracy and speed of its PCB assembly line’s quality inspection process.

Application Story
Defect Detection Difficulties
AOI process, the efficiency and effectivenessof inference models relies on the hardware
deployed to run them. These factors highlighta need for hardware that can handle andaccelerate complex AI algorithms withoutsacrificing accuracy. A process that consists ofimage data acquisition, defect detection,
followed by manual inspection and repair is nolonger competitive.

Companies encounter a range of difficulties
when it comes to maintaining a strict quality
assurance process, many of which are difficult
to account for due to the number of variables
involved. In traditional manufacturing
environments, human error is unavoidable.
Further, employees require time andexperience to develop the skills necessary tomeet high standards.
As demand grows, companies have the choiceof either increasing staff headcounts or risking
a lower rate of production due to the time ittakes for personnel to manually inspect eachcomponent of a PCB, which typically takes
between two and three seconds perphotograph.
A second challenge is faced by software
providers and systems integrators seeking tooffer complete quality inspection utilizing AI
inferencing. When instituting an AI-assisted

In the MAXER-2100, the vendor found theideal platform for their software, running theirinferencing model to rapidly identify defects
in PCB components, but the crucial thing that
the MAXER-2100 brought to the quality
inspection ecosystem was its use of using
deep learning to refine the client’s inferencing
model as it received and processed a greater
volume of data, resulting in a faster and moreaccurate quality inspection process overall.

Application StoryAOI Architecture
The Hawkeye solution acquired images of components, solder joints, traces, and other featuresof the PCBs via an AOI device. Once captured, these images were transmitted to the MAXER-2100,
powered by 13th Generation Intel® Core™ LGA 1700 socket-type processors. With NVIDIA® GPU
cards installed via PCIe slots, the MAXER-2100 was able to run the vendor’s AI inferencing model
to determine the pass/fail status of each component image.

These algorithms leveraged deep learning through approximately 30 photographs per component
at an inference rate of 0.03s, with which a baseline was established to assess components against.
The acceleration provided by the installation of additional GPUs in the MAXER-2100 worked toenhance the efficiency of these AI algorithms, shortening the time needed to detect defects tojust 0.05s per photograph.
Once defects were detected, detected defects were recorded in quality assurance reports
generated by the system. This report detailed visual data determining the location and nature of
the defect on a given component, so that manufacturers could take corrective action.
The key benefit of the MAXER-2100 as part of the solution as a whole was that once quality
reports had been generated, it could retrain and optimize the vendor’s software model throughdeep learning due to having more data with which to ascertain deviation from components thatpassed, and those that failed quality inspection.
.

Application Story
How AAEON’s Edge Server Offered MAXimum Impact
Enhanced Efficiency
With its support for both 12th and 13th Generation Intel® Core™ processors, the MAXER-2100
delivered could concurrently process high quantities of data, streamlining the inspection process
and reducing the judgment time to 0.05s per photograph, compared to between 2 and 3 seconds
per photograph required for manual inspection.

Improved Accuracy
The MAXER-2100’s GPUs provided the ideal platform to run the vendor’s defect detection
algorithm. As a consequence, the success rate of the application as a whole was over 99%,
improving on the estimated 95% accuracy rate offered by manual inspection.

Continual Improvement
Utilizing machine learning as data was received from the application’s AOI device, the MAXER2100 helped to refine the vendor’s inferencing model. Consequently, as the system analyzed
components and PCB defect reports were created, the model had a greater volume of data withwhich to determine variations in components.
The MAXER-2100’s impact on the vendor’s Hawkeye AOI system represents a leap forward in thefield of quality control, increasing both the efficiency of the manufacturing process and theprecision and reliability of AI-assisted quality inspection. By taking advantage of both theacceleration and processing offered by the edge server, the vendor’s model acquired more andmore detailed examples with which to distinguish between PBCs with defects and those thatreached quality standards, creating a continual improvement loop that enhanced the validity of
the generated reports, while also showcasing the exceptional value of AI integration.

Application Story
About AAEON
Established in 1992, AAEON is one of theleading designers and manufacturers ofindustrial IoT and AI Edge solutions. With
continual innovation as a core value, AAEON
provides reliable, high-quality computing
platforms including industrial motherboards
and systems, rugged tablets, embedded AI
Edge systems, uCPE network appliances, andLoRaWAN/WWAN solutions. AAEON also
provides industry-leading experience andknowledge to provide OEM/ODM services
worldwide. AAEON works closely with premierchip designers to deliver stable, reliable
platforms. For an introduction to AAEON’s
expansive line of products and services, visit
www.aaeon.com.

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AAEON Technology Inc.
6F., No. 28, Baogao Rd., Xindian Dist.,
New Taipei City 231029, Taiwan R.O.C.
+886-2-8919-1234
www.aaeon.com