Revolutionizing Electronic Design and Manufacturing: The Power of AI Technology

In recent years, the integration of Artificial Intelligence (AI) technology into various industries has revolutionized processes and improved efficiency. The electronic design and manufacturing sector is no exception. With its ability to analyze vast amounts of data, learn patterns, and make intelligent decisions, AI has become a game-changer in optimizing electronic design, enhancing manufacturing processes, and driving innovation. In this article, we will explore in detail the significant impact of AI technology on electronic design and manufacturing.

Design Optimization:

AI technology has transformed the electronic design process by enabling advanced optimization techniques. Designers can leverage AI algorithms to explore a vast design space, quickly evaluate numerous possibilities, and identify the most efficient and effective designs. AI algorithms can analyze design constraints, performance objectives, and historical data to generate optimized circuit layouts, improve power efficiency, reduce electromagnetic interference, and enhance overall design performance.

For example, AI-based optimization algorithms can analyze a wide range of circuit parameters and constraints to find the optimal trade-off between power consumption, performance, and cost. These algorithms can identify design solutions that human designers might overlook, leading to more efficient and innovative electronic designs.

Predictive Maintenance:

In the manufacturing phase, AI technology plays a crucial role in predictive maintenance. By continuously monitoring sensors and data points, AI algorithms can detect anomalies, predict equipment failures, and recommend preventive actions. This proactive approach minimizes unplanned downtime, reduces maintenance costs, and optimizes production efficiency.

AI-powered predictive maintenance algorithms can analyze real-time data, historical records, and equipment performance patterns to accurately forecast maintenance requirements and schedule interventions accordingly. For example, by monitoring equipment vibration, temperature, and power consumption data, AI algorithms can identify signs of impending equipment failure and alert maintenance teams to take preventive action. This ensures that equipment operates at peak performance, reduces costly downtime, and extends the lifespan of machinery.

Quality Control and Inspection:

AI technology enhances quality control and inspection processes in electronic manufacturing. Visual inspection, a critical aspect of quality control, benefits from AI-driven machine vision systems. These systems employ deep learning algorithms to analyze images and detect defects, such as soldering errors, component misalignment, or physical damage. By automating the inspection process, AI technology improves accuracy, reduces human error, and increases throughput, ultimately ensuring higher product quality.

AI-powered machine vision systems can quickly and accurately identify defects that may be difficult for human inspectors to detect. This technology not only improves the efficiency of quality control but also reduces the likelihood of defective products reaching the market, thereby enhancing customer satisfaction and brand reputation.

Supply Chain Optimization:

AI technology offers immense value in optimizing the electronic manufacturing supply chain. By analyzing historical data, market trends, and demand patterns, AI algorithms can accurately forecast material requirements, manage inventory levels, and optimize procurement processes. This helps to minimize supply chain disruptions, reduce excess inventory, lower costs, and enhance overall efficiency.

AI algorithms can analyze vast amounts of data to identify patterns and correlations, enabling more accurate demand forecasting. By leveraging this information, manufacturers can optimize their production schedules, avoid stockouts, and reduce inventory carrying costs. Additionally, AI-powered algorithms can continuously monitor market trends and supplier performance, allowing manufacturers to make informed decisions regarding sourcing strategies and supplier selection.

Process Automation:

AI technology enables process automation in electronic manufacturing, improving productivity and reducing manual labor. Robotic process automation (RPA) powered by AI algorithms can streamline repetitive and time-consuming tasks, such as data entry, documentation, and inventory management. This automation allows human workers to focus on higher-value tasks, leading to increased productivity, reduced errors, and improved overall workflow efficiency.

RPA can automate various tasks across the manufacturing process, such as generating production reports, updating inventory databases, or performing quality control checks.

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SMT 4.0 and CTF

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AI Industry 4.0-aided manufacturing should incorporate CTF quality methodology.

Increased use and power of artificial intelligence-aided electronics manufacturing, such as Industry 4.0, creates the opportunity and need to revisit how one manages and improves quality and defect rates.

Traditionally, industry has relied on approaches such as acceptance sampling plans (ASP), Six Sigma (SS), and statistical process control (SPC). Although these approaches offer important data-driven tools to flag and correct the root causes of defects, they generally do so with equal emphasis attached to detecting any or all levels of defect criticality.

The problem with these methods is certain quality outcomes of primary significance to the customer can be missed, as the example below illustrates. This example
serves to illustrate how another strategic method can be cooperatively implemented that will help avoid catastrophic defects. We will label this approach as CTF (critical to function) testing.

In this article, we will address two different branches of CTF testing:

  1. The first branch addresses appropriately linking customer criticality to the RQL (reject quality level) assigned and used in implementing acceptance sampling plans (ASP).
  2. The second branch addresses satisfying customer CTF testing that appends to continuous process approaches. This includes AI failure detection methods installed in the new breed of assembly systems.

Using CTF Testing to Adjust Sampling Plan RQL

As an example, assume the very hypothetical case of a laptop density-type PCB with 500 components and 10,000 solder joints.

After the solder stencil step, the typical way to calculate the post-stencil current reject defect level (RDL) is to divide the number of missing, insufficient, open lead, and bridged solder deposits by the total number of solder deposits needed. Let’s say there’s solder missing or defective in 12 of the spots in the area where a large, expensive 1,000 I/O µBGA part is to reside. If we calculate the defect percentage as function of the total number of solder deposits (10,000), the RDL is 0.12%.

If the standard RQL set for this kind of “critical” defect is RQL = 1% (with AQL set close to 0.1%), then generally the process running with RDL = 0.12% will pass the ASP testing about 95% of the time. This would probably be considered acceptable by the manufacturer and passed on to solder reflow steps. All would be considered as a smooth flow until after the PCB bearing the large BGA was electrically tested, three to four process steps later, and discovered to be nonfunctional.

By the time this is discovered, the manufacturer has two uncomfortable choices: one, very expensive surgical-level rework; the other, to scrap the assembled board, incurring significant financial loss.

Once the manufacturer becomes aware there are post-soldering failures under this critical area, it should react by raising the screening tests to a more critical RQL level, say RQL = 0.1%, requiring the production line to target test to a lower AQL (AQL = 0.01%).

This first-level reaction would be very costly to the manufacturer.

However, if the manufacturer at product production launch has the customer identify the CTF areas, and if those areas receive the extra level of inspection pre-reflow solder of say 100%, then the future disastrous post-stencil PCB could be caught before the disaster. This approach would be less costly.

Using AI within Industry 4.0 Systems to Improve Quality and Yields

When a company adds or replaces older assembly equipment with newer-AI capable equipment, the process engineer plays a bigger role than they did with their previous level of responsibility. Before taking advantage of the power of the AI-capable equipment, the engineer needs to know what’s right and wrong and the critical control variables with the key process steps, such as PCB planarity, stencil print control and solder paste variations.

In performing this CTF failure analysis, the process engineer must document the procedure that predicts process failure percentage, depending on all major failure modes. This is generally done by running designed experiments. With the help of a statistician, the ranking and variability of the key sources of defects can be predicted. These factors should be quantified mathematically by how they affect the overall defect rate (in this case, 0.12%). What this means is critical factors have a higher weighting (0.12% or 6/12+6/12+0), whereas minor factors have a negligible weighting (for example, 0.05%).

If one has a stable process, this weighting will remain the same indefinitely. However, there are generally changes of equipment, materials, temperature and a whole host of factors that impact the ranking of the causative defects on a continuous basis. Therefore, the control parameters need to be factored into an updatable system. As a result, the constant changes in defect rankings expand the job of the process engineer.

The greater the changes in each factory, the greater the need to reweight the CTF failure equation on an ongoing basis. The process engineer would then pass a first-level equation to the software engineer for implementation in AI. The process engineer then becomes responsible for updating the equation. For example, a major soldering equipment change or repair would certainly require an update to the equation. So might a change in humidity. Additionally, the process engineer would also be responsible for any CTF failure modes that are introduced into the process.

Another new task for the project engineer would be to incorporate the capability of troubleshooting ongoing process failures. For example, leading-edge post-stencil AOI systems are capable of detecting CTF problems if the system is taught where to look and what to do when defects are discovered. Hence, if CTF testing can immediately address solder defects when any, even one, occurs in that large CTF µBGA area, then continuous screening as monitored by AOI systems can produce RDL to desired levels.

If the customer successfully helps the manufacturer identify the CTF areas in advance and designates tests that isolate those areas, then the future disastrous electrical test failures can be caught post-solder stencil. At this step in the process, affected PCBs can be washed and re-stenciled and reintroduced into the line before the placement and soldering of components.


Much of the PCB assembly community uses a reactive quality test method, measuring defects on a lot-by-lot basis. The negatives to this approach are larger numbers of rejected product and materials that cost excess money and the possibility of losing business.

Implementation of artificially intelligent systems introduces the capability of continuous process monitoring and reporting. This opens the door for process engineers to implement more rigorous process improvements, since increased data create increased knowledge of the sources of defects.

The process engineer plays an important role in new state AI. In the example presented, they must identify and categorize the 12 PCB spots by doing a rigorous defect analysis on those spots. This increased knowledge should also focus the engineer on determining whether they are CTF or not.

The CTF concept sounds simple to implement, but it requires closer involvement between the process team and the customer.

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2018 AI And Robotic Predictions

2018 AI And Robotic Predictions

“In the information industry and at Thomson Reuters, AI and machine learning are already driving innovation and transformation. They are embedded in how we sift through large volumes of data and content and how we enhance, organize, connect, and deliver content and information. They are the engines underlying many of our products and services.” Dr. Khalid Al-Kofahi -Vice President, R&D and Head of the Center for AI and Cognitive Computing


When things go digital, they start following a new set of rules.

The rules of the physical world are either not applicable or are severely diminished. Things move from sparsity to abundance, where consumption does not lead to depletion. On the contrary, the more an object is consumed, the more valuable it becomes. Cost of production and distribution is no longer critical, and the concept of inventory is no longer applicable.

When things go digital, they also move from linear to exponential –  a world in which new technologies and new players can enter and dominate an industry in just a few years.

Consider that each year more people take online courses offered by Harvard than the number of students who attended Harvard in its 380-year history. Each year, three times more people use online dispute resolutions to resolve disputes on eBay than lawsuits filed in the United States. Each day, five billion videos are watched on YouTube. For context, the first YouTube video was uploaded in 2005. I was talking to a gentleman at Facebook a few weeks ago who said, “I joined Facebook three years ago and 70 percent of the company started after me.” Talk about hyper-growth businesses!

This is the environment that we operate in: Not only must we adapt, but we must help our customers adapt as well.

In the information industry and at Thomson Reuters, AI and machine learning (ML) are already driving innovation and transformation. They are embedded in how we sift through large volumes of data and content, and how we enhance, organize, connect, and deliver content and information. They are the engines underlying many of our products and services.



In the long term, our objective is to build personal digital assistants for knowledge workers. An assistant is an application that:

  • Interacts naturally with you
  • Is both responsive and proactive (without being intrusive)
  • The collection of all of your professional experiences
  • Available with a few words and a click
  • Learns from you as well as others (via their digital assistants)

Its purpose is not to replace you, but to augment you, to scale you, and to help you focus on more interesting tasks.

It will probably take a decade or two to build some of these digital assistants – but the near term is also full of interesting opportunities to transform, through simplification, automation, and machine assistance.

Research and Discovery

Research, discovery, and investigation represent a significant portion of what knowledge workers do. These are complex and time-consuming tasks, making them easy contenders for simplification, automation, and machine assistance.

Information Overload

Our world is connected and information-rich. The cycle of information creation is continuous and instant, and staying informed can be a daunting task. One of our primary objectives is to pivot away from customers finding information to the information finding the customer.

Risk and Compliance

This theme focuses on helping our customers comply with relevant laws and regulations, discover risks that could disrupt their businesses, and respond appropriately when things happen.

Making Sense

Knowledge work requires making sense of data in order to make time-sensitive and business-critical decisions. Whether it is a single document or collection of documents, an event, a work product, or an “abnormal” pattern, making sense is hard and time-consuming. AI can help.

This is just a selection of key focus areas based on analysis and discussions with our customers and business partners. The predictions in this report dive deeper into each of these opportunities. What is clear is that AI and machine learning are already here and their potential to assist knowledge workers is being realized.



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