Summary: Artificial intelligence is reshaping industries, yet a persistent AI skills gap limits workforce readiness. IEEE’s AI training and professional development programs bridge this divide, empowering employees and organizations to harness AI for innovation, efficiency, and growth.

AI isn’t just transforming technology, it’s revolutionizing how we work, innovate, and compete in the global marketplace. Yet despite AI’s growing prominence, a significant AI skills gap persists across industries. Many professionals and organizations are left struggling to harness AI’s full potential through effective AI education and professional development.

The AI Adoption Paradox in Professional Development

Recent research highlights a striking disconnect: while technology leaders identify AI as the most critical technology for 2025, most employees remain unclear on how to integrate AI tools into daily workflows. This gap represents both a challenge and an unprecedented opportunity for organizations seeking comprehensive AI training solutions.

IEEE’s global study, The Impact of Technology in 2025 and Beyond, surveyed 350 technology leaders—including CIOs, CTOs, and IT directors—and paints a compelling picture of AI’s strategic importance for workforce development. More than half ranked AI technologies, encompassing predictive and generative AI, machine learning, and natural language processing, as their top priority entering 2025.

The enthusiasm is backed by action: 

  • 20% of respondents regularly use generative AI in business applications, citing tangible operational value
  • 24% acknowledge AI’s benefits and plan to explore practical applications through structured AI education programs
  • 30% have high expectations and intend to experiment with smaller-scale AI training initiatives

Yet, this executive-level confidence doesn’t translate to the broader workforce.

Research shows that 84% of employees lack clarity about what generative AI is or how it functions in professional settings.

At the same time, 77% of workers feel inadequately trained in AI tools and remain uncertain about how artificial intelligence applies to their roles.

This disconnect creates a critical bottleneck: organizations eager to embrace AI transformation but lacking the skilled workforce to execute their vision.

The Strategic Imperative for AI Education and Skills Development

The stakes couldn’t be higher for professional AI training. Organizations that strategically deploy AI through professional training are positioned to significantly outperform competitors in growth, efficiency, and innovation.

Effective AI implementation enables companies to:

  • Make informed, data-driven decisions
  • Optimize resource allocation
  • Deliver personalized customer experiences
  • Streamline project management

Business leaders who understand AI’s capabilities and limitations through structured AI training will be better equipped to navigate the competitive landscape ahead.

However, the question isn’t whether to invest in AI education and professional development, it’s how to do it effectively and at scale through proven AI training programs.

IEEE AI Training and Professional Development

To address this critical skills gap, IEEE Educational Activities has developed a robust AI education ecosystem that bridges the divide between AI’s potential and practical implementation. These targeted AI training courses ensure employees gain both cutting-edge knowledge and hands-on skills to drive innovation.

Each course provides:

  • Professional development credits (PDHs and CEUs)
  • Shareable digital badges to showcase verified AI proficiency
Featured AI Training Programs
Advanced AI Training for Leaders

For organizations and individuals seeking comprehensive AI leadership development, IEEE has partnered with Rutgers University to launch the IEEE | Rutgers Online Mini-MBA: Artificial Intelligence program. This intensive AI education offering goes beyond technical training to address strategic AI implementation, helping participants understand how to leverage artificial intelligence for specific industries and job functions.

The mini-MBA program equips learners with advanced AI training to strategically address business challenges, optimize processes, maximize data effectiveness, enhance customer service, and drive overall organizational success through AI education. With both individual access and company-specific cohorts available, organizations can customize AI training experiences to meet their unique professional development needs.

Driving Innovation Through AI Skills Development

Whether you’re an experienced professional expanding your AI expertise or an organization looking to transform workforce capabilities, IEEE’s AI training programs provide the foundation for sustained innovation and growth.

Learn more about IEEE’s corporate solutions and professional development opportunities in artificial intelligence.

The semiconductor industry is at a critical inflection point. As devices shrink to nanometer scales and performance demands rise, traditional methods of ensuring packaging reliability are hitting their limits. Enter AI: the transformative force reshaping how we design, test, and optimize semiconductor packaging in today’s complex technological landscape.

The Growing Complexity Challenge

Modern semiconductor packaging faces unprecedented challenges as the industry rapidly expands. The global semiconductor packaging market is projected to grow from US$44 billion in 2025 to over US$90 billion by 2033, with packaging representing 20-25% of total manufacturing costs. 

However, this growth comes with significant reliability challenges. Packaging failures account for more than 65% of field returns in high-performance computing applications, while traditional reliability testing methods are proving inadequate for today’s advanced packaging technologies. The situation is further complicated by the growth of the chiplet market, expected to reach US$373 billion by 2030, where systems integrate components from multiple vendors using different materials, making reliability management without AI-assisted approaches virtually impossible. 

AI: The Game-Changing Solution

AI is revolutionizing semiconductor packaging reliability by enabling predictive analytics, real-time monitoring, and intelligent optimization. Unlike traditional methods that rely on historical data and simplified models, AI can process vast amounts of multi-dimensional data to identify patterns invisible to human analysis.

Machine learning algorithms and AI-driven predictive maintenance can significantly reduce time-to-failure prediction errors.

Research from IEEE reports improvements in AI-predictive accuracy ranging from 20% to over 90%, depending on the application and data quality.

This is achieved by moving away from scheduled or reactive maintenance to a proactive model that predicts failures before they happen.

Deep learning networks, particularly Long Short-Term Memory (LSTM) networks, have also found success in predicting semiconductor package lifecycles, with AI-enabled predictive maintenance reporting a reduction of equipment downtime by 30-50% and increasing machine life by 20-40%.

As Industry Adoption Accelerates, Real-World Applications Are Driving Transformation

The practical applications of AI in semiconductor packaging are already delivering measurable results across leading companies. The integration of AI with IoT sensors is creating new possibilities for real-time package health monitoring, enabling immediate corrective actions, and preventing failures and downtimes. 

Digital twin technology creates virtual replicas of physical packages that can simulate thousands of operational scenarios in minutes rather than months. Intel leverages AI-driven digital twins to accelerate semiconductor package development, simulating and optimizing performance of chips and manufacturing processes. This approach reduces development time by up to 25% and improves reliability before physical manufacturing begins.

Support Vector Machines (SVMs) are proving particularly effective for quality assurance, analyzing thermal imaging, electrical test data, and mechanical stress measurements simultaneously to identify defective packages. Samsung Electronics reported nearly a 50% reduction in failure analysis time after implementing such AI-driven classification techniques.

Build Expertise for Tomorrow’s Challenges

As the semiconductor industry embraces this AI transformation, staying current with the latest techniques becomes crucial. IEEE offers resources to help engineers navigate this evolving landscape.

The AI Applications in Semiconductor Packaging virtual training is a two-hour on-demand session that provides practical insights into how AI is transforming packaging reliability.

Participants will explore fundamental differences between traditional and AI-driven approaches, gaining deep understanding of machine learning, deep learning, and generative AI applications specific to semiconductor packaging. The training covers essential techniques including Support Vector Machines, K-Means clustering, and LSTM networks, with real-world applications in anomaly detection, digital twin modeling, and failure prediction.

Expert-Led Learning

Presented by Dr. Pradeep Lall, IEEE Fellow and MacFarlane Endowed Distinguished Professor at Auburn University. Dr. Lall brings unparalleled expertise with over 1,000 published papers, 50+ best-paper awards, and recognition from IEEE, ASME, SMTA, SEMI, and NSF. As Director of Auburn University’s Electronics Packaging Research Institute, he bridges academic rigor with industry practicality.

This training is part of IEEE’s comprehensive eLearning Library, accessible through IEEE Xplore and the IEEE Learning Network. Whether you’re a packaging engineer, AI specialist, reliability expert, or innovation leader, this program offers the knowledge and tools needed to leverage AI’s transformative potential.

The future of semiconductor reliability lies in intelligent systems that can predict, prevent, and optimize performance in ways previously unimaginable. The question isn’t whether AI will transform semiconductor packaging, it’s whether you’ll be ready to lead that transformation.

Artificial Intelligence (AI) is rapidly transforming the semiconductor industry, driving a new era of innovation, efficiency, and scalability. As demand for high-performance chips surges—fueled by generative AI, autonomous systems, and edge computing—semiconductor manufacturers are turning to AI to stay competitive and meet evolving market needs.

AI Innovation From Design to Production

From design to production, AI offers significant advancements across the semiconductor value chain. In chip design, AI enables faster development cycles by automating layout generation, logic synthesis, and verification. Leading companies now rely on machine learning and generative AI to streamline design workflows, reduce time-to-market, and enhance chip performance.

In fabrication, AI-powered visual inspection systems are outperforming human inspectors by detecting microscopic defects on wafers with greater accuracy. This not only improves yield but also reduces material waste and operational downtime. AI also plays a critical role in real-time process control, allowing fabs to dynamically adjust manufacturing parameters to optimize throughput, energy consumption, and equipment longevity.

Beyond the factory floor, AI is revolutionizing supply chain management. By forecasting demand, managing inventory, and mitigating disruptions, AI helps semiconductor companies navigate the complexities of global logistics with greater agility and precision.

Real-World Impact & Market Outlook

Major players in the industry are already integrating AI into their operations. TSMC, the world’s leading foundry, uses AI to classify wafer defects and generate predictive maintenance charts, significantly improving yield and reducing downtime. Samsung applies AI across DRAM design, chip packaging, and foundry operations to boost productivity and quality. Intel leverages machine learning for real-time defect analysis during fabrication, enhancing inspection accuracy and process reliability.

The AI boom is fueling unprecedented demand for advanced semiconductors.

TSMC projects its AI-related revenue to grow at a compound annual rate of 40% through 2029. As AI adoption expands, so does the need for more powerful, energy-efficient chips.

Looking ahead, AI will play a pivotal role in enabling autonomous manufacturing environments, where fabs self-optimize and self-correct. AI simulations will help discover novel materials for next-generation chips, while intelligent systems will reduce energy usage and carbon emissions across facilities.

Expand Your Knowledge

For professionals eager to deepen their understanding of AI’s transformative impact on semiconductor manufacturing, IEEE offers a comprehensive course series titled Mastering AI Integration in Semiconductor Manufacturing. This five-course program explores how AI enhances semiconductor production efficiency, optimizes processes, and improves product quality. Participants gain practical insights into evaluating AI’s impact on manufacturing operations, transitioning to predictive maintenance models, and applying real-world case studies to assess economic and technical outcomes.

Designed for AI engineers, edge computing specialists, semiconductor professionals, and researchers in nanotechnology and sustainability, the program bridges technical expertise with real-world applications—making it especially relevant as the industry evolves toward autonomous, adaptive systems.

Explore this course program today on the IEEE Learning Network (ILN), or contact an IEEE Content Specialist for institutional access!

AI is considered one of the most significant technological advancements in modern history and one that is having a major impact on every industry around the globe. The ability to understand AI applications and harness them to achieve next-level growth and operational success is key to true business innovation in every field.

Transforming the Face of Modern Business

The use of AI is bringing a new level of speed, efficiency, and productivity to a broad range of industry sectors and business functions.

From a product development perspective, AI accelerates development cycles and speed to market by analyzing market trends and consumer feedback, enabling companies to innovate faster and stay ahead of the competition. Through their ability to help automate tasks, analyze data, and optimize designs, AI tools ultimately support faster time-to-market for products.

In manufacturing and logistics, AI helps automate routine tasks, optimize supply chains, and manage inventory more effectively, allowing businesses to reduce operational costs and improve efficiency and productivity. According to a recent survey of international manufacturers, nearly 70% are already using AI solutions for everything from quality control and demand forecasting to predictive maintenance that enables them to proactively schedule equipment repairs before they result in costly downtime. BMW relies on AI algorithms to automate quality processes along its conveyor belt, while General Electric’s AI software helps the company employ its manufacturing resources more efficiently in order to achieve its sustainability goals.. 

The Future of AI in Business

In the field of enterprise security, AI helps companies protect data privacy and learn, adapt to, and stay ahead of cybersecurity threats. A recent Forbes study revealed that 51% of business owners surveyed are using AI to shore up their cybersecurity and fraud management activities. For example, Mastercard’s use of AI tools to scan payment data from partner banks helped the company avoid more than US$35 million in fraudulent payments over three years. Also, Amazon’s use of AI to analyze the nearly 750 million cyberattack incidents it logs daily enables the company to identify growing threats.

In the customer service arena, AI-powered chatbots and virtual assistants provide instant responses and create personalized experiences. Companies like Amazon, Walmart, Netflix, and South Korean video game developer Krafton are already streamlining their service processes and bringing greater depth to their customer interactions by offering personalized product recommendations, custom-optimizing search and browsing, more efficient customer service, and improved supply chain operations.

The significance of AI to business and the job market is clear, and while the debate over the proliferation of AI continues, one thing remains certain:

“AI will not replace humans. But those who use AI will replace those who don’t.”

-Ginni Rometty, former CEO of IBM

Let IEEE Help You Unleash the Power of AI for Yourself and Your Organization

Despite its integration into our daily lives, studies show that AI remains a source of confusion for many people. But given the widespread use of AI applications across so many industries, it’s crucial for business managers and other industry professionals to have a solid understanding of AI principles and their impact on business functions. The real challenge, and the ultimate success, doesn’t come from just learning about this transformative new technology, but from applying it effectively in your business.

Check out AI resources from IEEE to help you get up to speed on what you need to know:

The IEEE | Rutgers Online Mini-MBA: Artificial Intelligence Program is designed to demystify AI for business managers and leaders of all levels of understanding and experience with AI, providing them with the strategic insights needed to leverage AI effectively.

The program offers a non-IT view of AI and provides the foundational knowledge to assess AI’s analytical and decision-making capabilities. Learners explore how AI can be used to address business pain points, optimize processes, better serve customer needs, and improve an organization’s bottom line. The specialized 12-week course offers engaging real-world case studies, practical insights, forward-thinking ideas, and an invaluable Capstone Project, where learners will be able to complement their technical skills with a strategic, business view of AI and its real-world applications for themselves and their organizations.

Gain the expertise to navigate the complexities of AI in order to seamlessly integrate it into your operations, transform technological potential into a competitive edge, and innovate with impact. Learn more!

More eLearning courses on AI:

In 2025 and beyond, semiconductor sales—along with employment opportunities for engineers in the dynamic chip industry—are expected to rise precipitously.

Semiconductor sales are projected to hit nearly US$700 billion in 2025, grow to US$1 trillion by 2030, and potentially reach US$2 trillion by 2040, according to a Deloitte Insights report.

A number of trends are driving the semiconductor industry forward. First, post-pandemic sales of computers, tablets, smartphones, and other wireless and wired communications devices—which collectively accounted for nearly 60% of global semiconductor sales as of 2023-2024—are forecasted to experience strong growth during the next five to ten years. Additionally, demand for high-tech “generative AI chips” is on the rise. These chips enable computers’ central processing units (CPUs) to execute machine learning algorithms for everything from facial recognition applications to customer service-related chatbots, language processing for voice assistants, and more. 

On the design side, growth of the semiconductor market is supported by an increasingly popular chip manufacturing strategy known as “shift left,” which enables tasks that were once performed sequentially to be done concurrently for greater efficiency and cost savings.

Working to Meet Demand

To keep pace with projected growth, manufacturers are expanding capacity worldwide:

For example, after investing US$65 billion into chip fabrication facilities in Phoenix, Arizona in 2020, industry leader Taiwan Semiconductor Manufacturing Company (TSMC) recently announced additional investment of US$100 billion in order to double that location’s manufacturing capacity. Supported by almost US$8 billion in funding from the U.S. CHIPS (“Creating Helpful Incentives to Produce Semiconductors”) and Science Act of August 2022, key player Intel recently announced its plans to invest US$100 billion to expand its U.S-based domestic chip manufacturing capacity and capabilities in Arizona and Ohio. 

Elsewhere around the world, STMicroelectronics recently announced its intention to build a new, high-volume manufacturing facility in France. Semiconductor Manufacturing International Corporation (SMIC) is working to expand three of its existing Chinese facilities in Shanghai, Beijing, and Tianjin. Manufacturers Nvidia, AMD, and Micron have all announced plans to establish new operations in India.

A Skills Gap Persists

While worldwide sales of semiconductors, as well as manufacturing capacity to meet demand, are all on the uptick, one major challenge stands to potentially derail production: a global shortage of skilled workers.

In the U.S. alone, new semiconductor facilities are short by nearly 70,000 workers needed to staff them.

Of those positions, approximately 41% are in the engineering fields, 39% are technician roles, and another 20% are in computer science. This shortage threatens to impair the industry’s potential in the years to come, according to a study by the Semiconductor Industry Association (SIA). Furthermore, a recent report claimed that an estimated 400,000 additional professionals would be needed to fulfill Europe’s semiconductor industry goals, while China was some 30,000 workers short of meeting its semiconductor targets.

“Because semiconductors are foundational to virtually all critical technologies of today and the future,” the SIA study confirmed, “closing the talent gap in the chip industry will be central to the promotion of growth and innovation throughout the economy.”

Experts from Deloitte agreed, noting that the semiconductor field will need “electrical engineers to design chips and the tools that make the chips,” while “digital skills, such as cloud, AI, and analytics, are needed in design and manufacturing more than ever.”

Positioning Engineers for Success in Semiconductors and AI

To support workforce development, IEEE offers online learning programs that equip semiconductor professionals with cutting-edge AI and chip design skills. These include:

  • Artificial Intelligence and Machine Learning in Chip Design:
    Offered by IEEE Educational Activities in partnership with IEEE Future Directions and IEEE Global Semiconductors, this course program discusses the significance of artificial intelligence and machine learning. It provides an overview of how these technologies are shaping the future of chip design as well as key applications in design automation, relevant technologies, deployment considerations, and future prospects.
  • Integrating Edge AI and Advanced Nanotechnology in Semiconductor Applications:
    This five-course program created in partnership with the IEEE Computer Society helps learners understand the intersection of artificial intelligence, edge computing, and nanotechnology with real-life applications and future trends.
  • Semiconductor Manufacturing: Impact and Effectiveness of AI
    This course offers a comprehensive introduction to the evolving landscape of semiconductor manufacturing with special emphasis on the integration of artificial intelligence into this critical industry.

Upon successfully completing the programs, participants earn professional development credits, including Professional Development Hours (PDHs) and Continuing Education Units (CEUs). They’ll also receive a digital badge highlighting their proficiency in the technology area which can be showcased on social media.

For institutional access, contact an IEEE Content Specialist. Individuals can explore and enroll directly via the IEEE Learning Network.

 

Artificial intelligence (AI) isn’t just a buzzword. Its impact touches most of our lives every day.

For organizations, AI is currently being used to achieve a variety of business objectives. Applications include offering customers product recommendations to assisting with internal inventory management, reducing fraud and cybersecurity threats, operating digital personal assistants that save time, streamline processes, and enable businesses to make better use of their data, and more. But whatever its application, a 2024 McKinsey study reveals that AI is currently being employed in one way or another by over 70% of all companies worldwide. And AI’s role in organizations across every sector is only expected to grow in the future.

Despite AI’s growing presence in company operations around the globe, the reality is that AI remains a source of confusion for employees.

A whopping 84% of employees reported being unclear about what generative AI is or how it works, according to a 2024 survey by technology research and advisory firm Valoir. Similarly, 77% of all employees surveyed felt that they didn’t have adequate training in AI tools or that they fully understood how AI related to their jobs, according to the 2024 Digital Work Trends Report. Furthermore, managers didn’t fare much better than their employees. 73% of professionals at the managerial level confessed that they didn’t feel completely educated on, knowledgeable about, or trained in AI.

Implementing AI Strategies

Ultimately, strategic use of AI can significantly enhance the efficiency of business functions and processes. When AI takes over automating repetitive, manual tasks, employees have time to work on more productive, revenue-generating activities. It’s estimated that AI’s capabilities have the potential to automate tasks that account for up to 60-70% of the average employee’s time.

Because AI can analyze large amounts of data faster and at a scale beyond human capacity, it can open new doors to data analytics. Business activities that can benefit from the strengths of AI include forecasting revenue, predicting customer attrition, and identifying trends in employee retention. AI can also alerting professionals about the risk of customer fraud, manufacturing equipment breakdowns, and other potential issues in advance.

In the IT field, AI-driven detection models can be trained to boost security monitoring and identify and prevent BOTs and other cyber security threats from infiltrating a company’s enterprise systems.

Within a company’s financial functions, AI can be used to automate tasks, reduce mistakes, and save time and money. For example, payroll that’s manually processed contains an up to 8% level of human error. Properly-deployed AI and machine learning can help correct this issue.

Overall, the effective use of AI can help organizations enjoy more data-driven decision-making, improved resource allocation, more targeted and personalized customer experiences, more streamlined project management, and the delivery of more in-depth insights on market trends that can fuel new product development. As a result, business leaders who have a firm grasp on the benefits AI can deliver, how AI can be applied to their company’s operations, and how to properly deploy it will be better positioned for career success.“

For all the predictive insights AI can deliver, advanced machine learning engines often remain a black box,” acknowledged McKinsey & Company experts. However, it’s a challenge that business leaders are encouraged to face head-on to benefit their organization and their own career trajectory.

Let IEEE Empower You Through Education in AI

IEEE offers a range of educational resources that can help you better understand AI’s expanding role in business today so that your organization can harness and capitalize on its power.

Introducing the IEEE | Rutgers Online Mini-MBA: Artificial Intelligence Program
Designed to demystify AI for business managers and leaders, the new IEEE | Rutgers Online Mini-MBA: Artificial Intelligence program takes a strategic, non-IT view of AI. The program provides the foundational knowledge to assess AI’s analytical and decision-making capabilities. It will help you identify how AI can address business pain points, optimize processes, better serve customer needs, and improve an organization’s bottom line.

The highly specialized, 12-week program covers an introduction to AI as well as topics such as data analytics, process optimization, the benefits and application of AI to marketing and sales, customer service, the supply chain, and finance functions, ethics in AI, and the impact AI will have on careers, colleagues, and competencies in the future. The training features engaging real-world case studies, practical insights, forward-thinking ideas, and actionable strategies designed to help learners integrate AI into their operations. It also incorporates an invaluable capstone project experience, enabling students to take what they learned throughout the Mini-MBA program and apply those concepts to a specific business challenge.

Ideal for senior managers, directors, VP-level professionals, engineers, and young professionals looking to distinguish themselves in the job market, the IEEE | Rutgers Online Mini-MBA: Artificial Intelligence program delivers competitive advantages to both learners and their organizations by successfully complementing technical skills with a strategic, business overview of AI and its real-world applications. Learn more and save your seat today!

Flexible Online Learning Programs for Semiconductor Companies

IEEE has created several eLearning courses designed to enhance AI knowledge and skills that are key to individuals within the semiconductor industry. These educational resources will help ensure that employees are well-versed in the latest AI advancements and equipped with practical skills to drive innovation and strategically deploy AI for maximum success and efficiency within their organization. Resources include:

These course programs are also available to individuals through the IEEE Learning Network (ILN), providing flexible learning options for professionals at all levels.

Upon successfully completing the programs, participants earn professional development credits, including Professional Development Hours (PDHs) and Continuing Education Units (CEUs). They’ll also receive a shareable digital badge highlighting their proficiency in the technology area which can be showcased across various social media platforms.

If you are interested in obtaining institutional access to any of these programs through your organization, please contact an IEEE Content Specialist today.

 

Resources:

(30 May 2024). The State of AI in Early 2024. McKinsey & Company.

2024 Digital Work Trends Report. Slingshot.

February 2024). Language Matters: AI User Perceptions. Valoir.

Sterling, Terry. (30 January 2024). 11 Reasons Why Leaders Need to Understand Artificial Intelligence (AI). Balanced Scorecard Institute.

June 2023. The Economic Potential of Generative AI. McKinsey & Company.

Kempton, Beth. (22 August 2024). 10 Ways to Use AI in Business in 2025. Upwork.

Grennan, Liz, Kremer, Andreas, Singla, Alex, and Zipparo, Peter. (29 September 2022). Why Businesses Need Explainable AI—and How to Deliver It. McKinsey & Company.

Recent advances in edge computing and edge artificial intelligence (AI) are revolutionizing a broad range of industries and enabling a new age in predictive analysis and operational performance—so what exactly is edge AI and how is it changing the way businesses operate?

Edge AI refers to AI computations that are performed near the user at the “edge” of a network and close to where the data is located—which could be a retail store, a workplace, or an actual device such as a phone or a traffic light—rather than long distances away in a central cloud computing facility or private data center. Recent advances in machine learning and high-speed computing, along with the ongoing worldwide adoption of Internet of Things (IoT) devices that continue to deliver faster and more reliable connectivity, have led to the growing deployment of AI models at the edge.

Ultimately, one of the reasons why AI has been so successful when paired with edge computing is because modern-day AI algorithms have become increasingly sensitive to real-world issues and conditions. From the field of healthcare to agriculture and everything in between, AI has become more capable than ever of recognizing patterns and trends within the wide range of different circumstances that are present in real life. As a result, artificial intelligence is highly effective in edge applications and would be far less feasible, and, in some cases, even impossible to deploy in a centralized cloud or private data center. This is due to issues related to latency (delays in network communication), bandwidth (the amount of data that can be transmitted over a network in a specified amount of time), and privacy (the ability to control how personal data is collected, stored, and used).

Because edge technology performs analyses on data locally through decentralized capabilities, it can respond to user needs much quicker while also significantly reducing networking costs for an organization because it requires less internet bandwidth. Furthermore, the processing of data isn’t reliant on internet access, so mission-critical and time-sensitive AI applications can enjoy greater access and reliability. These edge computing benefits combined with the expanding flexibility and “intelligence” of AI neural networks are allowing organizations to capitalize on real-time insights at a lower cost and with greater security and privacy.

Edge AI Use Cases

Edge AI is being recognized as a pivotal technology that will continue to have a major impact on new product development, the streamlining of processes, and the user experience across a broad range of industries.

In the utility industry, for example, edge AI models are combining historical data, weather patterns, and other inputs to more efficiently generate and distribute energy to customers. 

In manufacturing, sensor data analyzed by edge AI technology is helping to predict when machines will fail and help factories avoid costly downtime.

Edge AI-enabled surgical tools in the healthcare field are helping doctors make real-time assessments in the operating room that improve surgical outcomes.

In the retail world, edge AI is working to enhance customer service by enabling the convenience of voice-based ordering by customers via smart speakers or other intelligent devices.

In the transportation sector, where real-time decisions can be the difference between life and death, edge AI is being used to adjust traffic lights to regulate traffic flow and reduce congestion.

And in the field of security across numerous organizations, edge AI’s real-time analysis of video footage can identify unwarranted activity and immediately inform authorities.

The Power of Edge AI and Nanotechnology in Semiconductor Applications

According to the authors of Artificial Intelligence in Nanotechnology, an academic white paper on the significant role AI can play in the development of nanotechnology, the incorporation of AI into nanotechnology—defined as the study and control of materials at the nano (molecular, atomic, or subatomic) level to create new, stronger, and more conductive materials and devices—has led to an exciting new vein of research and development called “AI-nanotechnology.”

Thanks to the big data that AI is able to analyze, semiconductors—made up of a wealth of nanoparticles—are immediately benefiting from the combination of edge AI and nanotechnology to design more efficient chips and bring them to market sooner.

Semiconductors, or chips, are components used to conduct or block electric current. They drive a bevy of modern-age devices, including mobile phones, computers, TVs, washing machines, LED bulbs, medical equipment, and more.

The use of edge AI is enabling semiconductor manufacturers to optimize their product’s power, performance, and area (or “PPA,” the three goals of chip design). It benefits PPA by helping engineers to design advanced new chips as well as to efficiently and cheaply overhaul and shrink many older-technology chip designs without needing to update fabrication equipment. By further integrating nanotechnology into this process and being able to design with new and existing materials at nano scales, manufacturers can cost-effectively create more robust semiconductors with improved functionality.

While both of these cutting-edge fields currently face a range of hurdles—ethics, privacy, and bias are issues for artificial intelligence, while nanotechnology struggles with regulatory, environmental, and safety concerns—experts contend that the integration of edge AI and nanotechnology “have the potential to work in concert to spur innovation and solve difficult problems….and [they] hold immense promise for revolutionizing various aspects of science, technology, and everyday life.”

Stay on the Cutting Edge of Continuing Education

A new five-course course program from IEEE, Integrating Edge AI and Advanced Nanotechnology in Semiconductor Applications, explores the intersection of artificial intelligence, edge computing, and nanotechnology through real-life applications and future trends. From the fundamentals of AI nanoinformatics to the specifics of semiconductor design, learners who complete the program will acquire a broad skill set enabling them to navigate the complexities of modern computing.

To learn more about accessing these courses for your organization, contact an IEEE Content Specialist today.

Interested in the course program for yourself? Visit the IEEE Learning Network.

 

Resources

Yeung, Tiffany. (17 February 2022). What is Edge AI and How Does It Work? NVIDIA.

(16 November 2023). Bringing AI to the Edge: How Edge AI is Revolutionizing Industries. Sintrones.

Agrawal, Radheyshree, Tilak Paras, Devand, Aryan, Bhatnagar, Archana, and Gupta, Piyush. (17 March 2024). Artificial Intelligence in Nanotechnology. Springer Nature.

Nanotechnology. National Geographic.

Brode, Bernie. (21 March 2022). AI and Nanotechnology are Working Together to Solve Real-World Problems. Stack Overflow Blog.

2023 Edge AI Technology Report. Chapter I: Overview of Industries & Application Use Cases. Wevolver.

semiconductor-workforce-development

Semiconductors are the brains behind so many devices and processes that we take for granted today, from computers, smartphones, cars, programmable coffee makers, and washing machines to high-tech robotics, augmented reality and virtual reality systems, satellites used in national defense, and more. Based on their widespread use in such a broad range of technologies, semiconductors are critical to life in modern industrialized societies — and this reality was further validated by the supply chain issues and shipping delays experienced during the pandemic.

Government Support for the Semiconductor Industry

To help strengthen the United States’ competitiveness and resilience in the semiconductor arena, the CHIPS (“Creating Helpful Incentives to Produce Semiconductors”) and Science Act, enacted in August 2022, earmarked nearly US$53 billion for domestic research and manufacturing. It also established a 25% tax credit for capital investments in semiconductor manufacturing. Europe soon followed suit with their own version of this initiative, The European Chips Act, in September 2023.

Since then, the U.S. government has already disbursed some US$29 billion in CHIPS Act funds to eight companies— Intel, Micron, Global Foundries, Polar Semiconductor, TSMC Arizona Corporation (a subsidiary of Taiwan Semiconductor Manufacturing Company), Samsung, BAE Systems, and Microchip Technology— in an effort to reinvigorate semiconductor manufacturing domestically. This funding succeeded in catalyzing the establishment of a range of new manufacturing facilities, including Intel’s new factories in Arizona, New Mexico, Oregon, and Ohio as well as Micron’s new US$100 billion chip plant in Syracuse, New York.

The European CHIPS Act has driven similar investment in Europe’s semiconductor industry in hopes of doubling the EU’s global market share from 10% to 20% by 2030. “The governments of nearly every major economy are pouring tens of billions of dollars into semiconductor industries every year,” confirmed Chris Miller of Nature Reviews Electrical Engineering, all in an effort to stake claim in a robust global semiconductor market that forecasting organization World Semiconductor Trade Statistics predicts will grow by over 13% to US$588 billion in 2024 and hit US$1 trillion in global revenue by 2030.

The problem? There aren’t currently enough semiconductor technicians and engineers to meet the demand created by the CHIPS Acts and other global initiatives. For example, the U.S. government expects there will be a need for 100,000+ semiconductor technicians and as many as 300,000+ engineering graduates by 2030 to support the growing industry.

Initiatives in Semiconductor Workforce Development Training

In response, companies and educational institutions alike are taking creating and resourceful approaches to filling the talent gap.

As broadcasted in a June 2024 PBS NewsHour segment, Intel Vice President of Talent Planning and Acquisition Cindi Harper confirmed that Arizona-based Intel has recently invested hundreds of millions of dollars into workforce development and that its new semiconductor plants will create 10,000 jobs at the company.

“We have high-paying jobs that are extremely interesting, [and] the manufacturing side of it isn’t what you would have seen 30 or 40 years ago,” agreed Greg Jackson, Director of Facility Operation at Phoenix, AZ-based Taiwan Semiconductor Manufacturing Company, in the PBS NewsHour segment.

And a broad range of colleges, universities, and online educational platforms worldwide are further supporting the semiconductor workforce development movement by offering certificate programs in everything from semiconductor fabrication, devices, packaging, microelectronics, AI in semiconductor design (a strategy which is helping manufacturers enjoy greater efficiency and speed to market), and more.

Let IEEE Unlock the Door to Opportunity

Get started with specialized training, Artificial Intelligence and Machine Learning in Chip Design. Delve into the ways in which artificial intelligence (AI) and machine learning (ML) techniques are revolutionizing chip design methodologies. This training provides engineers with essential knowledge to leverage AI and ML effectively in chip design and electronic design automation (EDA). Learners will identify high-value applications and gain insight into optimizing design methods and preparing for the future of chip design.

Upon successful completion of the program, learners will earn an IEEE Certificate of Completion bearing professional development hours (PDHs) and continuing education units (CEUs). Get started today!

For institutional access, contact a Sales Specialist.

Resources

David, Emilia. (7 June 2024). Where the CHIPS Act Money Has Gone. The Verge.

Shakir, Umar. (25 July 2023). EU Will Spend €43 Billion to Stay Competitive on Chip Production. The Verge.

Miller, Chris. (11 January 2024). Global Chip War for Strategic Semiconductors. Nature Reviews Electrical Engineering.

Khalid, Asma. (19 December 2023). Biden Has Big Plans for Semiconductors. But There’s a Big Hole: Not Enough Workers. NPR.

(7 September 2022). How Semiconductor Makers Can Turn a Talent Challenge Into a Competitive Advantage. McKinsey & Company.

2024 KPMG Global Semiconductor Industry Outlook. KPMG.

(27 May 2022). Purdue Launches Nation’s First Comprehensive Semiconductor Degrees Program. Purdue University News.

Allan, Liz. (16 October 2023). Chip Industry Talent Shortage Drives Academic Partnerships. Semiconductor Engineering.

Hilson, Gary. (5 March 2024). STEM Education Scales to Strengthen Chip Sector Skills. EE Times.

Sy, Stephanie and Jackson, Lena. (11 June 2024). How Arizona is Building the Workforce to Manufacture Semiconductors in the U.S. PBS News.

Based on the ability of artificial intelligence (AI) to automate repetitive tasks and process massive amounts of data, AI technology is revolutionizing many industries. Such industries range from healthcare and banking to cyber security, transportation, marketing, customer service, manufacturing, and more.

One industry that’s undergoing a particularly significant transformation at the hands of AI technology is the field of semiconductor design.

The Landscape for Semiconductors

Semiconductors, also called chips, microchips, or integrated circuits, are tiny components that enable electronic switching and serve as the foundation for all computer processing. As a result, semiconductors are integral to everything from smart phones and laptops to wind turbines, solar technology, wearable technology (like fitness trackers), electronic control systems and driverless capabilities in modern vehicles, implantable medical technology (like pacemakers and insulin pumps), gaming hardware, and many more technologies consider essential in today’s industrialized economies.

As sales of connected technologies continue to grow, so does demand for the next-generation semiconductors needed to fuel them. According to Statista, the global market for semiconductors is expected to grow by 13% to nearly US$590 billion in 2024. At the same time, the semiconductor industry is highly competitive. Taiwan, South Korea, and Japan currently lead the world in semiconductor production. However, experts expect the landscape will get even more competitive. The United States and European Union are vigorously ramping up their activity following their enactment of The CHIPS and Science Act and The European CHIPS Act in August 2022 and September 2023, respectively.

In the semiconductor industry’s ongoing quest for tools that can enhance engineering efficiency and accelerate speed to market, thereby giving manufacturers a competitive edge, the use of artificial intelligence and machine learning (ML) stand as game-changers in semiconductor design and manufacturing.

A New Paradigm in Design

Experts confirm that the use of AI enhances semiconductor (chip) design, or the process known as “electronic design automation” (EDA), in many ways.

Among them, AI automates complex processing tasks, thereby reducing the risk of human error. Artificial intelligence’s ability to analyze past patterns across huge quantities of data, identify efficient pathways, and optimize the space (or “real estate”) within semiconductors helps improve semiconductor performance and meet design criteria. It also reduces chip size, resources required, and cost. By being able to “learn” from past experiences, AI algorithms help semiconductor engineers predict and prevent potential design issues down the road that could otherwise result in the need for costly changes.

Ultimately, AI helps semiconductor manufacturers optimize power, performance, and area, or “PPA”– the three goals of chip design– by helping engineers to both design advanced new chips as well as efficiently and cheaply overhaul and shrink the many older-technology (65 nanometer process node or larger) chip designs on which much of the semiconductor industry has been predicated for the past decade without the need to update their fabrication equipment.

The future continues to look bright for the integration of AI in semiconductor design, with Deloitte experts noting that “some chips are getting so complex that advanced AI may soon be required.”

Learn the Ins and Outs of AI in Semiconductor Design from an Industry Expert

In today’s fast-paced technological landscape, AI and ML techniques are revolutionizing chip design methodologies. Integrated-circuit (IC) chip companies and engineers have unprecedented opportunities to use these technologies to enhance product quality across crucial dimensions such as speed, energy efficiency, and cost. This, in turn, enables the achievement of goals with reduced engineering resources and accelerated time-to-market.

Stay on top of the dynamic field of AI in semiconductor design through a two-day virtual training from IEEE, Artificial Intelligence and Machine Learning in Chip Design. It is presented by Andrew B. Kahng, an IEEE Fellow, Distinguished Professor of CSE and ECE at the University of California San Diego, and co-founder of Blaze DFM, Inc., an EDA software company that delivered new cost and yield optimizations at the IC design-manufacturing interface.

This comprehensive two-day virtual training session will equip engineers with:

  • The essential knowledge to leverage AI and ML effectively in chip design and EDA,
  • An understanding of the rationale behind these technological shifts to identifying high-value applications and selecting relevant AI and ML technologies, and
  • Insights into optimizing design methods and preparing for the future of chip design.

Attendees will also have the opportunity for first-hand interaction with Professor Kahng and ask him questions during the interactive question-and-answer portion of the training.

Successful completion of this training and assessment will earn attendees an IEEE Certificate of Completion bearing professional development hours (PDHs) and continuing education units (CEUs).

Don’t miss this opportunity to get your questions answered directly by a renowned subject matter expert in the industry! Save your seat today to secure your spot in this enlightening training session.

Interested in access for yourself? Visit the IEEE Learning Network (ILN).

Connect with an IEEE Content Specialist today to learn how to get access to this program for your organization.

 

Resources

Anirudh, VK. (10 February 2022). 10 Industries AI Will Disrupt the Most by 2030. Spiceworks.

(2 February 2024). How AI is Transforming the Semiconductor Industry in 2024 and Beyond. ACL Digital.

McCallum, Shiona. (3 August 2023). What Are Semiconductors and How Are They Used? BBC.

(29 March 2024). Generative AI: The Next S-Curve for the Semiconductor Industry? McKinsey & Company.

Loucks, Jeff, Stewart, Duncan, Simons, Christie, and Kulik, Brandon. (30 November 2022). AI in Chip Design: Semiconductor Companies are Using AI to Design Better Chips Faster, Cheaper, and More Efficiently. Deloitte. 

Alsop, Thomas. (8 February 2024). Semiconductor Market Revenue Worldwide from 1987 to 2024. Statista.

debate-over-ai-pros-and-cons

Few technologies have been by equal measures as captivating and controversial as the ongoing emergence of artificial intelligence (AI).

Most recently, the tech universe was rocked following the November 2023 firing and unprecedented rehiring of Sam Altman, CEO of OpenAI, a leading artificial intelligence company and maker of ChatGPT. That same month, and roughly a year after its breakthrough introduction in November 2022, ChatGPT announced the release of a powerful upgraded version that, among other capabilities, allows users to make their own customized chatbots and include chatbot creations in the company’s new “GPT Store.” The latest version of ChatGPT also offers a new legal “shield” that reportedly protects professional users against claims of copyright infringement.

However, with the continued growth of AI and on the tail of the aforementioned developments, a host of repercussions and concerns have emerged. Among them, AIHungry.com’s research on Google Trends revealed that a search for the term “AI Taking Jobs” reached record-high levels in November 2023. Furthermore, that the term experienced a 400% increase in search activity between November 2022 and November 2023.

While these statistics reflect society’s unease with what it sees as a growing reality, experts agree that those who don’t understand AI or how to use it are at the greatest risk of being replaced by it. This underscores the importance of acquiring new skills as a way to gain a competitive advantage and future-proof yourself from workplace developments like automation.

The Pros and Cons of AI

According to a recent summary of key statistics and predictions collected from prominent industry sources, publications, and market research firms, the growth and evolution of AI will potentially drive a mixed bag of results. These results are expected to have both positive and negative ramifications on the future of society and the workplace. Among them:

  • AI could replace up to one billion jobs worldwide over the coming decade— though it may also create 97 million new jobs by 2025.
  • One out of three businesses surveyed in a recent study claimed to be replacing at least some human functions in their workflow with AI solutions.
  • Administrative/repetitive functions, as well as jobs in such fields as bookkeeping and proofreading, are most at risk of being replaced by AI solutions. Manual labor jobs, as well as those requiring creativity and/or interpersonal skills (such as writing and legal services), are reportedly at the least risk of being replaced by AI.
  • In a recent study, one in four employees surveyed in the U.S. believes that their job may be replaced by an AI solution in the next five years, with 37% expressing concern over the possibility of this displacement.
  • On the other hand, nearly 20% of workers in that study welcomed the growth of AI based on their belief that it will relieve them of some tedious/repetitive tasks. 85% of those surveyed support the move towards automation for “hazardous or unhealthy” jobs.
  • Three out of four employees surveyed in another study, however, believe that the widespread adoption of AI will end up driving inequality in the workplace, with women being at 10% greater risk of job loss due to automation than their male counterparts.

Government Oversight

As the debate over AI rages on between stakeholders worldwide to determine how the technology can best help– not hurt– citizens, companies, and employees, calls for governmental parameters around the use of artificial intelligence are growing louder.

As shared during an October 2023 hearing by the U.S. Senate Committee on Health, Education, Labor and Pensions’ Subcommittee on Employment and Workplace Safety, a joint World Economic Forum and Accenture report revealed that some 40% of the 19,000 individual tasks across 867 occupations studied could be impacted and/or replaced by the ‘large language model’ (LLM) tools used by AI. With generative AI expected to impact everything from the state of both existing and future jobs to privacy, legal, and ethical considerations and more, industry leaders in the U.S. are asking Congress to establish a “rational, risk-based” regulatory framework for AI that will take the needs of employers, employees, and other constituents into consideration.

The U.S. White House Office of Management of Budget supported this request in October 2023 by asking each of its executive agencies to designate a Chief AI Officer (CAIO) to be in charge of “advancing responsible AI innovation” and “managing risks from the use of AI.” According to the official White House memo, “Artificial intelligence (AI) is one of the most powerful technologies of our time [and] we must seize the opportunities AI presents while managing its risks….particularly those affecting the safety and rights of the public.”

Stay on the Cutting-Edge of AI

The world of AI remains a moving target. With AI systems “advancing so rapidly and unpredictably that even on the rare occasions lawmakers and regulators have tried to tackle them, their proposals quickly become obsolete,” according to New York Times journalists Karen Weise and Cade Metz.

The rapid forward motion of AI will have ramifications on the global labor pool. A summary of key statistics and predictions reports that 120 million workers worldwide will need “upskilling” in the next three years due to developments in artificial intelligence. The key to avoiding AI job automation, according to the report? “Creativity, emotional intelligence, and STEM skills.”

Are you on top of the full extent of AI’s direction, impact on society and business, and evolving design requirements? Are you shoring up your skill sets to minimize the risk of replacement by automation? AI-related course programs from IEEE are designed to keep learners abreast of the many opportunities, challenges, and considerations to be taken into account when developing, planning, using, or training for the expansion of artificial intelligence across its many applications.

Artificial intelligence-related courses from IEEE include:

Resources

(1 November 2023). US Senate Subcommittee Focuses on AI in the Workplace. IAPP.

(1 November 2023). White House OMB Issues AI Memorandum to Federal Agencies. IAPP.

Miller, Jim. (11 November 2023). AI Replacing Jobs Statistics: 40 Automation and AI Stats for 2023. AIHungry.com. 

Weise, Karen and Metz, Cade. (8 December 2023). The Morning: AI’s Big Year. The New York Times.

Wilson, Mark. (November 2023). ChatGPT Gets its Biggest Update So Far – Here are 4 Upgrades That Are Coming Soon. TechRadar. 

Perrigo, Billy. (22 November 2023). Sam Altman Returns as OpenAI CEO. Here’s How It Happened. Time.

(September 2023). Jobs of Tomorrow: Large Language Models and Jobs. World Economic Forum/Accenture.

Lufkin, Braun. (18 April 2022). What ‘Upskilling’ Means for the Future of Work. BBC.