WASHINGTON, D.C. — As artificial intelligence transitions from abstraction to reality, U.S. adults are evenly divided on its implications for humankind. Forty-nine percent say AI is “just the latest in a long line of technological advancements that humans will learn to use to improve their lives and society,” while an equal proportion say it is “very different from the technological advancements that came before, and threatens to harm humans and society.”

Despite this split assessment, a clear majority (59%) say AI will reduce the need for humans to perform important or creative tasks, while just 38% believe it will mostly handle mundane tasks, freeing humans to do higher-impact work.

And perhaps reflecting AI’s potential to diminish human contributions, 64% plan to resist using it in their own lives for as long as possible rather than quickly embracing it (35%).

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Majorities Expect AI to Eclipse the Telephone, Internet in Changing Society

Americans may not be convinced that AI poses a threat to humanity, but majorities foresee it having a bigger impact on society than did several major technological advancements of the past century.

Two-thirds (66%) say AI will surpass robotics in societal influence, and more than half say it will exceed the impact of the internet (56%), the computer (57%) and the smartphone (59%). Just over half (52%) think AI will have more impact than the telephone did when it was introduced.

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Familiarity Breeds Comfort?

Americans’ perceptions of the impact AI will have on society don’t differ much by gender, age or other characteristics. Most demographic groups are closely split over whether AI is just the next technological thing versus a novel threat. But attitudes vary significantly by people’s exposure to AI.

Seventy-one percent of daily users of generative AI (programs like ChatGPT and Microsoft Copilot that can create new content, such as text, images and music) say AI is just another technological advancement. By contrast, only 35% of those who never use generative AI agree.

This 36-percentage-point gap contrasts with smaller differences between users and nonusers of other AI applications in confidence that AI can be harnessed for good. There is a 27-point difference between users and nonusers of virtual assistants (like Amazon Alexa and Apple Siri) in their view that AI will benefit humans. And there are roughly 20-point differences in this endorsement of AI between users and nonusers of personalized content (such as apps that make movie and product recommendations) and smart devices (like robotic vacuums and fitness trackers).

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Personalized Content Now Routine; Generative AI Still Novel

ChatGPT reportedly became the fastest-growing app ever, after it was launched publicly in November 2022. However, adoption of generative AI, generally, among U.S. adults is still sparse relative to other types of AI. Less than a third of U.S. adults currently report using generative AI tools either daily or weekly. About a quarter use them less frequently than that, while 41% don’t use them at all.

At the same time, more than four in 10 adults say they use voice recognition/virtual assistants (45%) or smart devices (41%) at least weekly. And nearly two-thirds (65%) report frequent use of personalized content.

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Demographic Gaps Greatest for Generative AI Adoption

The broad adoption of personalized content is reflected in the relative uniformity of its use across demographic groups. The same is true for virtual assistants and smart devices, except that — possibly reflecting their expense — the use of smart devices is greater among upper- than middle- and lower-income groups and, relatedly, among college-educated and employed adults. Smart devices are also the one technology used more often by women (44%) than men (37%).

On the other hand, there are sizable differences by age, education, employment and gender in the use of generative AI.

• The rate of using generative AI daily or weekly is highest among 18- to 29-year-olds (43%) and lowest among seniors (19%).
• There is an eight-point difference by gender, with more men (36%) than women (28%) using it. However, the gender gap is greater among adults 50 and older than among those 18 to 49.
• Employed adults (37%) are nearly twice as likely as nonworking adults (20%) to be regularly using generative AI.

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Bottom Line

While Americans are split over whether AI is a routine step in the evolution of technology or a unique threat, most expect it to diminish the need for human creativity and are hesitant to fully adopt it personally. For now, positive views of AI are closely linked with people’s experience with it, rather than their personal demographics. The implication is that as usage expands, acceptance may follow.

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Heterogeneous integration is a key enabler of today’s AI innovations. By bringing together multiple chips with different functionalities, a.k.a., chiplets, AI devices have been able to achieve tremendous performance gains. However, the heterogeneous integration of advanced packages has its own set of process control obstacles that must be addressed, including new interconnect challenges involving redistribution layers (RDL) and bond pads.

Recently, Onto Innovation and Samsung Electronics Co., Ltd., teamed up to explore how picosecond ultrasonic technology could be used to measure the metal thickness of RDL and bond pads in high performance AI packages. In this blog, the second in our series on the advanced packaging applications of picosecond ultrasonic technology, we will show how this technology can be used to measure metal films during RDL and bond pad processes.

But first, a word about picosecond ultrasonic technology, a widely adopted non-contact, non-destructive acoustic technique that can be used to measure film thickness.

Measuring films

Picosecond ultrasonic technology measures film thickness by tracking the round-trip travel time of ultrasonic waves generated and detected using an ultrafast laser pump probe technique. A short laser pulse (pump) creates an acoustic wave that travels through the film, reflects at material interfaces, and returns to the surface. A second laser pulse (probe) detects the returning wave.

Two detection methods can be used to determine film thickness or properties:

• REF mode senses changes in surface reflectivity caused by the returning wave.
• PSD mode detects surface deformation by measuring shifts in the reflected probe beam.

By measuring the time it takes for the wave to return and knowing the speed of sound in the material, the film thickness can be accurately determined to sub-angstrom levels.

This level of layer-specific metrology, precision, and measurement repeatability is increasingly critical as AI-driven packaging pushes the limits of interconnect density and uniformity.

Accuracy and repeatability

For the purpose of our exploration, we conducted a test to confirm the accuracy of picosecond ultrasonic technology when measuring the films typically used in advanced packaging. These metals include Au, Ni, physical vapor deposition (PVD) seed Cu, and RDL Cu (EP). For each film, we used picosecond ultrasonic technology to measure wafers of varying thicknesses. Then we cut the wafers for cross-section analysis and estimated the correlation with the picosecond ultrasonic results for the four films (Figure 1). In this scenario, the correlation factor R² was higher than 0.99 for all four cases, with the slope close to one, demonstrating the accuracy of picosecond ultrasonic measurements.

This level of correlation is not only impressive, it is essential. Competing technologies such as four-point probe (4PP) or contact profilometry often fall short in multilayer structures or non-planar surfaces, where mechanical contact can distort results or damage delicate features.

Fig. 1: Correlations between picosecond ultrasonic measurements and cross-section analysis for Au, Ni, seed Cu (PVD), and RDL Cu (EP). The excellent correlation factors demonstrate the accuracy of picosecond ultrasonic technology.

Following this, we measured product wafers in various interconnect processes with picosecond ultrasonic technology, including seed Cu/Ti measured in REF mode (Figure 2) and RDL in PSD mode (Figure 3). RDL thickness can be measured both in pre- and post-seed Cu removal.

Fig. 2: Measurement signal of seed Cu/Ti in REF mode. Delay time for seed Cu and Ti are indicated by the red arrows.

Fig. 3: RDL Cu signal after the seed Cu etch process. The red arrow shows the round-trip time of an acoustic wave within RDL Cu film.

The horizontal axis in Figures 2 and 3 represents the time delay of the probe pulse with respect to the pump, while the vertical axis represents the change of reflectivity (ΔR/R) caused by the travelling acoustic wave. The sharp change of reflectivity in the signal, as demonstrated in Figures 2 and 3, is mostly due to the acoustic wave reflected from the film interface returning to the surface. In addition, the position of the peak and trough is shown with red arrows. These arrows are directly related to the thickness of the films, seed Cu, barrier Ti, and EP Cu. From the position of the peak and trough, the thickness of each film can be calculated. For seed Cu and barrier Ti, the repeatability of each layer is 0.3% or less of the thickness for all measurements. This demonstrates the capability of picosecond ultrasonic technology to meet 10% gage repeatability and reproducibility requirements.

For RDL Cu, the sharp change of reflectivity near 2,200 picoseconds (ps) corresponds to the round-trip time of the acoustic wave within the RDL Cu film; Cu thickness can be calculated from the trough position. The sharpness of the trough, along with thickness, indicates the trough position can be calculated with good repeatability. In fact, the repeatability of RDL Cu measurements for each point is less than 0.1% of Cu thickness, once again exceeding the 10% gage repeatability and reproducibility requirements.

Such precision is a necessary technical achievement. As AI applications demand tighter control over signal integrity and power efficiency, the margin for error in interconnect thickness shrinks dramatically. Legacy tools simply cannot keep up.

Measuring bond pads with dimple structures

We also used picosecond ultrasonic technology to measure a bond pad with a dimple structure. The film stack is composed of Au/Ni/Cu, with Au being the top film. Although the height of the center region of the pad is lower than the surrounding region by a few microns, we successfully measured individual layer thicknesses by measuring a few sites in the outer ring area and selectively choosing ones with good signal-to-noise ratios. This is possible because the focused spot size of the picosecond ultrasonic beam is 8×10µm², small enough for the direct measurement on the outer ring of the pad.

This is another area where contact-based methods struggle. The ability to selectively target small, non-planar regions without physical interference is a key differentiator of picosecond ultrasonic technology.

Fig. 4: An example of an REF mode signal from the bond pad with a dimple structure for Au (a), Ni and Cu (b).

In Figure 4 a-b, the red arrows indicate the reflectivity changes caused by the acoustic waves returning from the interface to the surface. With these peak positions, we were able to calculate each layer’s thickness with good accuracy and repeatability. The repeatability of Au, Ni and Cu films for each measurement was less than 0.2%, 0.05% and 0.05%, respectively. As such, all three film measurements outperformed the requirement of 10% gage repeatability and reproducibility.

It should be noted that Au film is much thinner than the other two films. As such, there is a significantly higher repeatability for Au films compared with the other films.

Conclusion

The AI era is upon us, and it would not be possible without advanced packaging innovations. However, the complexity of today’s advanced packaging is worlds away from traditional packaging. Today’s back-end process involves a variety of technologies and requires new methods of process control. In addition, controlling metal thickness and within wafer uniformity in these processes is critical to meeting the requirements for signal integrity in advanced packaging.

Unfortunately, some fabs still rely on legacy metrology tools like 4PP or contact profilometry—technologies that were never designed for the complexity of modern AI packages. These tools often introduce mechanical stress, lack the resolution to resolve thin or buried layers, and cannot reliably measure non-planar or dimpled structures.

As we have demonstrated, picosecond ultrasonic technology is an ideally suited interconnect metrology solution for both RDL and bond pads. This technology offers excellent accuracy and gage capability for the control of interconnect processes in advanced packaging.

As back-end processes demand the same level of precision, uniformity, and control traditionally associated with front-end requirements, picosecond ultrasonic technology can play a major role in advanced packaging metrology, delivering the non-contact, high-resolution, and repeatable measurements that AI applications demand.

Acknowledgments

We would like to thank Dae-Seo Park, Sanghyun Bae, Junghwan Kim, and Hwanpil Park of Samsung Electronics Co., Ltd., and Kwansoon Park, G. Andrew Antonelli, Robin Mair, Johnny Dai, Manjusha Mehendale and Priya Mukundhan of Onto Innovation for their contributions to this article.