TOKYO – Saki Corporation, an innovator in the field of automated optical and X-ray inspection equipment, will be once again exhibiting at Smart SMT & PCB Assembly 2024, an annual trade exhibition focused on semiconductor surface mount/printed circuit board manufacturing to be held in Suwon, Korea from February 21 to 23, 2024. Show visitors are invited to visit booth I 102 to discover Saki’s latest automated inspection solutions including SPI, AOI and the award-winning X-ray Automated Inspection System (AXI).

As a total solution for in-line automated inspection for SMT processes, Saki's latest hardware and software will demonstrate the high-speed, high-precision automated inspection capability realized by Saki's latest hardware and software.

Alongside its co-exhibitors JS TECH and H&J Corporation, the Saki technology team will present the company’s latest equipment lineup for high-speed, high-precision SPI-AOI-AXI in-line inspection:

• 3Xi-M110 (AXI) - the industry's fastest automated X-ray inspection solution and winner of a Global Technology Award 2023;
• 3Di-LS3 (AOI) - advanced AOI with on-site camera module upgrade capability;
• 3Di-LS2 (AOI) - with 18µm resolution camera module;
• 3Si-LS2 (SPI) - same rigid gantry structure and drive unit as AOI providing high-speed, high-precision solder inspection;
• BF-Sirius (2D-AOI) - benchtop 2D-AOI with UV illumination for conformal coating inspection.

In addition to its machine solutions, Saki will also present its versatile software suite which works with the equipment to form a total solution for quality inspection:

• One Programming / Saki Link function (reference exhibit) - Software system for batch operation and centralized management of SPI, AOI, and AXI on a line-by-line basis;
• QD Analyzer – Saki’s powerful SPC system that contributes to improved product quality and productivity by accumulating and statistically analyzing the operating status and inspection results of all equipment on the production line;
• AI Solutions (reference exhibit) - an introduction to Saki’s unique integration of AI functionality.

“We are very pleased to introduce Saki's latest solutions for SMT processes to our customers in Korea, Asia, and all over the world at Smart SMT & PCB Assembly again this year,” said Mr. Kim Kyu Seob, General Manager of Saki Corporation Korea Representative Office. “Our solutions help our customers solve their challenges by promoting more automation, reducing labor, and eliminating the need for specialized skills in the manufacturing process. We invite you to visit our booth for a complete overview of Saki's latest solutions.”

For more information about Saki visit www.sakicorp.com/en/ 

INGLESIDE, IL – IDENTCO – a manufacturer of high-performance labeling solutions for the power equipment, electronics, transportation, and general industrial sectors – has renamed its popular stock TTL ® 100 Series. Now called DuraTrack, the label series for printed circuit boards and electronic components provides printed circuit assemblies with comprehensive traceability – an increasingly attractive internal quality control and supply chain transparency feature for brand owners producing high-leverage electronics.

Engineered for use in surface mount technology and throughout the entire assembly process, DuraTrack thermal transfer printable labels can endure harsh fluxes, the latest cleaning chemistries, and high temperatures encountered in today’s circuit board assembly processes on both sides of the board.

The DuraTrack Series of labels is ideally suited to a wide array of electronics components manufacturing environments, including box builds, internal and external vehicle parts (such as in-cabin infotainment systems and outside sensors), and various other computer unit and PCB assembly settings.

Available in 61 sizes, in both high-performance polyimide and polyester, this off-the-shelf product is available for immediate delivery. The DuraTrack Series can also be customized for those customer applications that require an exact fit.

WASHINGTON – As one of the world’s fastest-growing economies, and arguably home to Latin America’s most attractive business environment, the Dominican Republic is a leading candidate for nearshored investments in advanced manufacturing activities for the U.S. and regional markets—particularly electronics like printed circuit boards (PCBs) and the assembly, test, and packaging (ATP) of semiconductors—according to a new report by the Information Technology and Innovation Foundation (ITIF).

Particularly as the U.S. government stimulates semiconductor-sector growth across North America through the CHIPS and Science Act, the Dominican Republic has a unique opportunity to establish a presence for itself in global semiconductor and PCB value chains that are projected to grow 40 percent to become a $1 trillion industry by 2030, the report finds.

“Global production chains in advanced-technology industries are undergoing a dramatic reordering,” said Stephen Ezell, vice president of global innovation policy at ITIF and author of the report. “Between geopolitical tensions with China, the search for lower production-cost environments, a desire to tap into new pools of skilled talent, and to locate production closer to end users, the Dominican Republic offers a stable political economy and one of the most attractive environments for foreign direct investment in the Western hemisphere.”

To start, the Dominican Republic offers a cost-competitive manufacturing environment. For example, the World Bank finds that the hourly labor cost in the Dominican Republic is just six percent of the U.S. rate, approximately half that of Costa Rica or Mexico, and even less than in China. Not to mention, notable global electronic manufacturers such as the Eaton Corporation and Rockwell Automation are housed in the Dominican Republic.

In addition, a key driver of the Dominican Republic’s economic growth is the country’s 87 free zones that underpin advanced manufactured goods production—notably of electronics products. These free zones support 820 companies and employ close to 200,000 workers. The Dominican Republic’s second-largest export (after medical devices) is electronics, accounting for $1.2 billion in 2022. On top of that, the Dominican Republic’s liberalized trade regime permits exporters duty-free access to more than 900 million consumers across 49 countries—made in large part thanks to the Dominican Republic-Central America-United States Free Trade Agreement, its Economic Association Agreement with the European Union, and its membership in the initial World Trade Organization (WTO) Information Technology Agreement. The Dominican Republic sits in an enviable geographical position in the Caribbean and offers world-class logistical infrastructure.

Yet despite all the benefits the Dominican Republic offers for increased semiconductor manufacturing, there are some areas where the country lags. If the Dominican Republic is truly to move up the value chain in advanced electronics manufacturing, it will need to educate and field a skilled workforce. Thankfully, the Dominican Republic already possesses a technical education ecosystem and demonstrates the ability to support the workforce of a high-tech electronics manufacturing industry.

The report concludes with several policy recommendations to help advance the Dominican Republic’s ambition to compete in semiconductor and PCB value chains:

• The Dominican Republic should be considered a leading candidate as a designated recipient of funding from the U.S. Department of State’s International Technology Security and Innovation (ITSI) Fund.
• The government of the Dominican Republic should develop an explicit semiconductor value proposition and broader competitiveness strategy, which addresses topics such as expanding the pool of scientists and engineers, how the government can better support applied industrial R&D activity, and how it can further improve regulatory, tax, customs, and incentive programs to attract globally mobile investment in the sector.
• The Dominican Republic should launch an awareness campaign reaching out to global investors in advanced electronics industries highlighting the country’s free zones and tax policies.
• The Dominican Republic should help address the global semiconductor workforce gap by expanding the availability of degree programs in electrical engineering, computer science, and related courses, including in collaboration with leading U.S. universities in these fields.
• The Dominican Republic needs to increase the number of individuals holding IPC 610 certifications.
• The Dominican Republic could consider the use of investment incentives to attract semiconductor industry manufacturing activity.
• The Dominican Republic should set up a “one-stop-shop” to facilitate the regulatory clearance of all permits and approvals, such as environmental review permits, that would be required to launch a semiconductor ATP or PCB facility in the country.
• The Dominican Republic should join the expanded Information Technology Agreement (ITA-2) and join discussions toward promulgating an ITA-3.
• The Dominican Republic should champion robust digital trade regulations. For example, one way to do so would be by joining the WTO’s Joint Initiative on E-commerce.
• Brazil and Ecuador have entered into bilateral protocols relating to trade rules and transparency with the United States. The Dominican Republic should explore the possibility of entering into an exchange of views for a similar protocol with the United States.

“As leading semiconductor manufacturers evaluate where to situate a multi-billion dollar fab or ATP investments, they may consider as many as 500 discrete factors, ranging from countries and states’ talent; tax, trade, and technology policies; labor rates; laws; custom policies; and more”, said Ezell. “Since the ease and certainty of doing business in a country matters greatly, the Dominican Republic is well poised. But, there’s still work to do.”

Read the report.

CAMBRIDGE, UK – Artificial Intelligence is transforming the world as we know it; from the success of DeepMind over Go world champion Lee Sedol in 2016 to the robust predictive abilities of OpenAI’s ChatGPT, the complexity of AI training algorithms is growing at a startlingly fast pace, where the amount of compute necessary to run newly-developed training algorithms appears to be doubling roughly every four months. In order to keep pace with this growth, hardware for AI applications is needed that is not just scalable – allowing for longevity as new algorithms are introduced while keeping operational overheads low – but is also able to handle increasingly complex models at a point close to the end-user.

Drawing from the “AI Chips: 2023–2033” and “AI Chips for Edge Applications 2024–2034: Artificial Intelligence at the Edge” reports, IDTechEx predicts that the growth of AI, both for training and inference within the cloud and inference at the edge, is due to continue unabated over the next ten years, as our world and the devices that inhabit them become increasingly automated and interconnected.

The why and what of AI chips

The notion of designing hardware to fulfill a certain function, particularly if that function is to accelerate certain types of computations by taking control of them away from the main (host) processor, is not a new one; the early days of computing saw CPUs (Central Processing Units) paired with mathematical coprocessors, known as Floating-Point Units (FPUs). The purpose was to offload complex floating point mathematical operations from the CPU to this special-purpose chip, as the latter could handle computations more efficiently, thereby freeing the CPU up to focus on other things.

As markets and technology developed, so too did workloads, and so new pieces of hardware were needed to handle these workloads. A particularly noteworthy example of one of these specialized workloads is the production of computer graphics, where the accelerator in question has become something of a household name: the Graphics Processing Unit (GPU).

Just as computer graphics required the need for a different type of chip architecture, the emergence of machine learning has brought about a demand for another type of accelerator, one that is capable of efficiently handling machine learning workloads. Machine learning is the process by which computer programs utilize data to make predictions based on a model and then optimize the model to better fit with the data provided, by adjusting the weightings used. Computation, therefore, involves two steps: Training and Inference.

The first stage of implementing an AI algorithm is the training stage, where data is fed into the model, and the model adjusts its weights until it fits appropriately with the provided data. The second stage is the inference stage, where the trained AI algorithm is executed, and new data (not provided in the training stage) is classified in a manner consistent with the acquired data.

Of the two stages, the training stage is more computationally intense, given that this stage involves performing the same computation millions of times (the training for some leading AI algorithms can take days to complete). As such, training takes place within cloud computing environments (i.e. data centers), where a large number of chips are used that can perform the type of parallel processing required for efficient algorithm training (CPUs process tasks in a serialized manner, where one execution thread starts once the previous execution thread has finished. In order to minimize latency, large and numerous memory caches are utilized so that most of the execution thread’s running time is dedicated to processing. By comparison, parallel processing involves multiple calculations occurring simultaneously, where lightweight execution threads are overlapped such that latency is effectively masked. Being able to compartmentalize and carry out multiple calculations simultaneously is a major benefit for training AI algorithms, as well as in many instances of inference). By contrast, the inference stage can take place within both cloud and edge computing environments. The aforementioned reports detail the differences between CPU, GPU, Field Programmable Gate Array (FPGA) and Application-Specific Integrated Circuit (ASIC) architectures, and their relative effectiveness in handling machine learning workloads.

Within the cloud computing environment, GPUs currently dominate and are predicted to continue to do so over the next ten-year period, given Nvidia’s dominance in the AI training space. For AI at the edge, ASICs are preferred, given that chips are more commonly designed with specific problems in mind (such as for object detection within security camera systems, for example). Digital Signal Processors (DSPs) also account for a significant share of AI coprocessing at the edge, though it should be noted that this large figure is primarily due to Qualcomm’s Hexagon Tensor Processor (which is found in their modern Snapdragon products) being a DSP. Should Qualcomm redesign the HTP such that it strays from being a DSP, then the forecast would heavily skew in favour of ASICs.

AI as a driver for semiconductor manufacture

Chips for AI training are typically manufactured at the most leading-edge nodes (where nodes refer to the transistor technology used in semiconductor chip manufacture), given how computationally intensive the training stage of implementing an AI algorithm is. Intel, Samsung, and TSMC are the only companies that can produce 5 nm node chips. Out of these, TSMC is the furthest along with securing orders for 3 nm chips. TSMC has a global market share for semiconductor production that is currently hovering at around 60%. For the more advanced nodes, this is closer to 90%. Of TSMC’s six 12-inch fabs and six 8-inch fabs, only two are in China, and one is in the USA. The rest are in Taiwan. The semiconductor manufacture part of the global supply chain is therefore heavily concentrated in the APAC region, principally Taiwan.

Such a concentration comes with a great deal of risk should this part of the supply chain be threatened in some way. This is precisely what occurred in 2020 when a number of complementing factors (discussed further in the “AI Chips: 2023 – 2033” report) led to a global chip shortage. Since then, the largest stakeholders (excluding Taiwan) in the semiconductor value chain (the US, the EU, South Korea, Japan, and China) have sought to reduce their exposure to a manufacturing deficit, should another set of circumstances arise that results in an even more exacerbated chip shortage. This is shown by the government funding announced by these major stakeholders in the wake of the global chip shortage.These government initiatives aim to spur additional private investment through the lure of tax breaks and part-funding in the way of grants and loans. While many of the private investments displayed pictorially below were made prior to the announcement of such government initiatives, other additional and/or new private investments have been announced in the wake of them, spurred on as they are by the incentives offered through these initiatives.

A major reason for these government initiatives and additional private spending is the potential of realizing advanced technology, of which AI can be considered. The manufacture of advanced semiconductor chips fuels national/regional AI capabilities, where the possibility for autonomous detection and analysis of objects, images, and speech are so significant to the efficacy of certain products (such as autonomous vehicles and industrial robots) and to models of national governance and security, that the development of AI hardware and software has now become a primary concern for government bodies that wish to be at the forefront of technological innovation and deployment.

Growth of AI chips over the next decade

Revenue generated from the sale of AI chips (including the sale of physical chips and the rental of chips via cloud services) is expected to rise to just shy of USD$300 billion by 2034, at a compound annual growth rate of 22% from 2024 to 2034. This revenue figure incorporates the use of chips for the acceleration of machine learning workloads at the edge of the network, for telecom edge, and within data centers in the cloud. As of 2024, chips for inference purposes (both at the edge and within the cloud) comprise 63% of revenue generated, with this share growing to more than two-thirds of the total revenue by 2034.

This is in large part due to significant growth at the edge and telecom edge, as AI capabilities are harnessed closer to the end-user. In terms of industry vertical, IT & Telecoms is expected to lead the way for AI chip usage over the next decade, with Banking, Financial Services & Insurance (BFSI) close behind, and Consumer Electronics behind that. Of these, the Consumer Electronics industry vertical is to generate the most revenue at the edge, given the further rollout of AI into consumer products for the home. More information regarding industry vertical breakout can be found in the relevant AI reports.

For more information regarding key trends and market segmentations with regards AI chips over the next ten years, please refer to the two reports: “AI Chips: 2023–2033” and “AI Chips for Edge Applications 2024–2034: Artificial Intelligence at the Edge”.