New Algorithms to Improve X-Ray Inspection
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The drive towards miniaturization has created increasing challenges to the overall failure analysis and quality inspection of electronic devices. This trend has equally challenged the image quality of x-ray inspection systems – engineers need to see more details in each inspection. Image quality is paramount to the ability of making actionable decisions on the information acquired from an x-ray machine. Previous generations of x-ray technologies have focused on hardware improvements – better x-ray sources and better x-ray sensors. Although further improvements can still be achieved in hardware, our focus will be on the latest wave of technology breakthroughs and innovation in radiography systems: algorithms. Algorithms have been paramount in enabling the inspection of challenging electronics assemblies. From computed tomography to dual energy algorithms, these special pieces of software may be the difference between finding and not finding that issue with your sample. In this presentation we will go behind the curtains and see how these algorithms are designed. The examples presented will illustrate real life situations that show the extent of the use of algorithms in radiography for failure analysis and quality control.
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Epoxy flux provides a low-cost, high-reliability solution for package-on-package (PoP) assembly that combines soldering and reinforcement into a single-step reflow process. Epoxy flux can be applied by dipping or jetting the packages in the flux prior to assembly. During reflow, the epoxy flux forms solder joints while also curing to reinforce the joints. This eliminates additional underfilling steps and equipment required by other assembly methods. Epoxy flux offers reliability advantages over underfilling such as preventing solder extrusion during rework.
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2) The four categories of PIC microcontrollers - baseline, mid-range, enhanced mid-range, and PIC18 - with descriptions of their features.
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Epoxy flux a low cost high reliability approach for pop assembly-imaps 2011
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More from Bill Cardoso
Counterfeit Detection Using X-Ray Image as a Fingerprint
Taggants have been widely adopted in the fight against counterfeit components. The process of applying taggants to components consists of first determining the authenticity of the part. This step can be achieved by either knowing that the component is authentic (by a well-documented chain of custody from the original component manufacturer), or by running a batch of tests to sufficiently determine the conformity of the component to its original specifications.
Taggants are often chemical compounds with a unique mix of elements that are embedded into the surface of the component. This unique mix effectively introduces a fingerprint to the component, and the combination of component identification and fingerprint is carefully uploaded to a database. Later in the supply chain, the taggant can be read back by special sensors to verify that the component and the attached fingerprint match as in the database. Some of the shortcomings of this method include the need to prepare this unique compound, the need to correctly apply it to the surface of the component, and the need to use expensive sensors to read back the fingerprint of the component at the point of use. There is also the risk that the taggant might end up in the wrong hands, which would allow such individuals to criminally authenticate counterfeit components.
The other major shortcoming of taggants relate to the authenticity protection of printed circuit board assemblies (PCBA). PCBAs can be considered electronic systems, each with hundreds or thousands of electronic components. Thus, the application of a unique taggant to each component is not feasible.
In this work, we introduce the use of the x-ray image as the unique fingerprint for an electronic component or PCBA. Unique features of the x-ray image such as solder voids, cracks, part alignment, die attach porosity and voiding, die placement and alignment, and wire bonding diagram. These are just a few of the many features in the x-ray image that can be used in tandem to create a unique fingerprint for a single component or an entire PCBA. This technique can also be expanded to mechanical objects by utilizing other idiosyncratic features of the part – such as voids and porosity – to generate the x-ray image fingerprint.
How X-ray Technology is Improving the Electronics Assembly Process
It was 1895 when Wilhelm Roentgen discovered a mysterious light that allowed him to see through things – and called it x-rays. Since then, x-rays have been adopted in a wide range of applications in the electronics assembly process. Instead of taking a “deep-dive” in one aspect of x-ray inspection, in this paper we will cover a broad range of applications:
Electronic component inspection.
Real-time defect verification.
Counterfeit detection.
Component counting and material management.
Reverse engineering.
2D, 2.5D, and 3D x-ray inspection.
Voids, bridging, and head-in-pillow failures in bottom terminated components
Design for manufacturing (DFM) and design for x-ray inspection (DFXI).
Artificial Intelligence
We will also discuss how artificial intelligence (AI) is changing the way we think about x-ray inspection. Things we would never dream of doing just a few years ago are now reality by combining AI and x-ray inspection. Moreover, we will show you a series of real-life cases on how our team of AI scientists is using AI to solve the most challenging applications in x-ray inspection. And beyond x-ray inspection, we’ll examine how AI is forever changing the way in which we manufacture and inspect anything.
Finding hardware hacks using x-rays
Bloomberg recently reported that an attack by Chinese spies reached almost 30 U.S. companies, including Amazon and Apple, by compromising America’s technology supply chain, according to extensive interviews with government and corporate sources.
In 2016 we presented a solution to this threat at the SMTA Symposium on Counterfeit Parts and Materials. X-ray inspection and AI are the key technologies we deploy to make sure this threat is excluded from our supply chain.
iPhone X: Steve Job's iPhone and Advanced Packaging
It’s been 10 years since Steve Jobs introduced the iPhone to the world. Much has happened since then. Over this past decade, the iPhone became a reference design, and the object of desire of a legion of fans who wait anxiously for every launch of the Cupertino company. Undoubtedly, the most advanced iPhone in the market today, the iPhone X is a technology marvel. The double stacked boards, dual battery, and a face recognition sensor bring the iPhone X to a whole different level.
In this presentation, we’ll explore these technological advances by a live teardown of the iPhone X. The teardown will be followed by a detailed coverage of the technical details of critical parts of the device. This live teardown will be accompanied by x-ray and CT images of the iPhone X, so the audience will get unprecedented insights on what makes this iPhone tick. More importantly, we will explore the assembly process utilized to put the iPhone X together. This presentation is targeted at a wide technical audience looking for a better understanding on how advance consumer electronics are designed and assembled.
2 d, 2.5d, and 3d x ray inspection – what’s a “d”?
In this presentation we clarify one of the most confusing topics in x-ray inspection: the difference between 2D, 2.5D, and 3D x-ray inspection. Check the slides to see if you can tell if an image is 2D, 2.5D, or 3D!
10 Ways to Find Counterfeit Components Using X-Rays
How long can you go without interacting with an electronic device? Needless to say our lives have became dependent on a network of electronic system - from the plane you flight to the breaking system in your car to the monitoring systems at hospitals. Unfortunately, counterfeit electronic components have permeated our supply chain. From medical, to industrial, to military, to aerospace systems, we are all susceptible to the counterfeit electronic threat. That means that you may think you're buying component A, while in fact a criminal is selling you component B. The economic incentive relies on the fact that criminals can make component B look like component A, while component B can cost a fraction of component A. Huge profits can then be realized if component B can be sold as A. The wrong component in the wrong place and time can have catastrophic consequences. Imagine if component B ends up in a missile system, or in an airplane, or in a hospital, or in the breaking system in your car.
What can you do about it? The only solution is to test your electronic parts before assembly. In this presentation Creative Electron shows 10 innovative ways to find counterfeit electronic components using radiography. We have examples of several powerful techniques that include the need to perform 100% inspection, the use of metadata to assess component authenticity, and the need to inspect both the interior and exterior of the components.
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These slides were presentation at the Radiation 101 class at Creative Electron, July 7, 2012. Also included how to set up radiation monitoring network with iRad sensors.
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Counterfeit Detection Using X-Ray Image as a Fingerprint
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2 d, 2.5d, and 3d x ray inspection – what’s a “d”?
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10 Ways to Find Counterfeit Components Using X-Rays
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In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
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Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
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4. Organizational processes and structures that may inhibit effective AI adoption.
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5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
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The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
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Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
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Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
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New Algorithms to Improve X-Ray Inspection
- 1. New Algorithms to Improve X-Ray Inspection
- 4. This*century,*algorithms*are*driving*x.ray*innovation • Significant*improvement*in*computing* power • Explosive*algorithm*focus*– see*Google • Marginal*improvement*in*hardware 4
- 5. Outline • SMT*Defects*identified*with*x.ray* inspection • WISE • Dual*Energy • ICARUS • Artificial*Neural*Networks 5
- 7. WISE:*Wavelet*Image*Spectra* Enhancement* • X.ray*inspection*enhancement*algorithm*based* on*parametric*estimation*of*image • Runs*in*real*time*– not*an*offline*filter • What*is*parameter*estimation? PATENT PENDING Raw data s = A.sin(2πf + ϕ) A = 2 f = 4kHz ϕ = π Parameter Estimation 8
- 16. 17
- 17. 18
- 18. Dual*Energy*X.Ray*Imaging PATENT PENDING Low Energy Image High Energy Image 19
- 19. Dual*Energy*X.Ray*Imaging PATENT PENDING Dual Energy Image 20
- 25. ICARUS Image Comparison Algorithms for Radiography Unified Software 26
- 28. ICARUS Image*Comparison*Algorithms*for*Radiography*Unified*Software 1st – SELECT REFERENCE IMAGE 2nd – DEFINE COMPARISON MASK 29
- 29. ICARUS Image*Comparison*Algorithms*for*Radiography*Unified*Software 4th – CORRECT FOR ROTATION AND OFFSET TRANSLATION 3rd – COLLECT IMAGE FROM TAPE 30
- 30. ICARUS Image*Comparison*Algorithms*for*Radiography*Unified*Software 5th – MEASURE SIMILARITY BETWEEN REFERENCE AND ACQUIRED IMAGE WITH MASK APPLIED TO BOTH IMAGES REFERENCE ACQUIRED 31
- 31. ICARUS Image*Comparison*Algorithms*for*Radiography*Unified*Software 6th – COMPARE MEASURED SIMILARITY WITH SET THRESHOLD REFERENCE ACQUIRED Example: 98% SIMILARITY: IF < THRESHOLD ! FAIL IF > THRESHOLD ! PASS DIFFERENCES 32
- 32. ICARUS Image*Comparison*Algorithms*for*Radiography*Unified*Software REFERENCE ACQUIRED + DIFFERENCES 33
- 33. ICARUS Image*Comparison*Algorithms*for*Radiography*Unified*Software 7th– REPEAT PROCESS FOR WHOLE TAPE. STOP ON FAIL 34
- 34. Conclusions • Innovation*in*the*first*century*of*the*x.ray* inspection*market*was*driven*by* hardware*improvements • Massive*computing*power*has*allowed* the*second*century*to*be*driven*by* algorithm*innovations • Expect*to*see*more*algorithm* improvements*this*century 35