When Yifeng Chen was a teenager in Shantou, China, in the early 2000s, he saw a TV program that amazed him. The show highlighted rooftop solar panels in Germany, explaining that the panels generated electricity to power the buildings and even earned the owners money by letting them sell extra energy back to the electricity company.

Yifeng Chen

Employer

Trina Solar

Title

Assistant vice president of technology

Member Grade

Member

Alma Maters

Sun Yat-sen University, in Guangzhou, China, and Leibniz University Hannover, in Germany

An incredulous Chen marveled at not only the technology but also the economics. A power authority would pay its customers?

It sounded like magic: useful and valuable electricity extracted from simple sunlight. The wonder of it all has fueled his dreams ever since.

In 2013 Chen earned a Ph.D. in photovoltaic sciences and technologies, and today he’s assistant vice president of technology at China’s Trina Solar, a Changzhou-based company that is one of the largest PV manufacturers in the world. He leads the company’s R&D group, whose efforts have set more than two dozen world records for solar power efficiency and output.

For Chen’s contributions to the science and technology of photovoltaic energy conversion, the IEEE member received the 2023 IEEE Stuart R. Wenham Young Professional Award from the IEEE Electron Devices Society.

“I was quite surprised and so grateful” to receive the Wenham Award, Chen says. “It’s a very high-level recognition, and there are so many deserving experts from around the world.”

Trina Solar’s push for more efficient hardware

Today’s commercial solar panels typically achieve about 20 percent efficiency: They can turn one-fifth of captured sunlight into electricity. Chen’s group is trying to make the panels more efficient.

The group is focusing on optimizing solar cell designs, including the passivated emitter and rear cell (PERC), which is the industry standard for commodity solar panels.

Invented in 1983, PERCs are used today in nearly 90 percent of solar panels on the market. They incorporate coatings on the front and back to capture sunlight more effectively and to avoid losing energy, both at the surfaces and as the sunlight travels through the cell. The coatings, known as passivation layers, are made from materials such as silicon nitride, silicon dioxide, and aluminum oxide. The layers keep negatively charged free electrons and positively charged electron holes apart, preventing them from combining at the surface of the solar cell and wasting energy.

Chen and his team have developed several ways to boost the performance of PERC panels, hitting a record of 24.5 percent efficiency in 2022. One of the technologies is a multilayer antireflective coating that helps solar panels trap more light. They also created extremely fine metallization fingers—narrow lines on solar cells’ surfaces—to collect and transport the electric current and help capture more sunlight. And they developed an advanced method for laying the strips of conductive metal that run across the solar cell, known as bus bars.

Experts predict the maximum efficiency of PERC technology will be reached soon, topping out at about 25 percent.

IEEE Member Yifeng Chen displays an i-TOPCon solar module, which has a production efficiency of more than 23 percent and a power output of up to 720 watts.Trina Solar

“So the question is: How do we get solar cells even more efficient?” Chen says.

During the past few years he and his group have been working on tunnel oxide passivated contact (TOPCon) technology. A TOPCon cell uses a thin layer of “tunneling oxide” insulating material—typically silicon dioxide—which is applied to the solar cell’s surface. Similar to the passivation layers on PERC cells, the tunnel oxide stops free electrons and electron holes from combining and wasting energy.

In 2022 Trina created a TOPCon-type panel with a record 25.5 percent efficiency, and two months ago the company announced it had achieved a record 740.6 watts for a mass-produced TOPCon solar module. The latter was the 26th record Trina set for solar power–related efficiencies and outputs.

To achieve that record-breaking performance for their TOPCon panels, Chen and his team optimized the company’s manufacturing processes including laser-induced firing, in which a laser heats part of the solar cell and creates bonds between the metal contacts and the silicon wafer. The resulting connections are stronger and better aligned, enhancing efficiency.

“We’re trying to keep improving things to trap just a little bit more sunlight,” Chen says. “Gaining 1 or 2 percent more efficiency is huge. These may sound like very tiny increases, but at scale these small improvements create a lot of value in terms of economics, sustainability, and value to society.”

As the efficiency of solar cells rises and prices drop, Chen says, he expects solar power to continue to grow around the world. China currently leads the world in installed solar power capacity, accounting for about 40 percent of global capacity. The United States is a distant second, with 12 percent, according to a 2023 Rystad Energy report. The report predicts that China’s 500 gigawatts of solar capacity in 2023 is likely to exceed 1 terawatt by 2026.

“I’m inspired by using science to create something useful for human beings, and then driven by the pursuit for excellence,” Chen says. “We can always learn something new to make that change, improve that piece of technology, and get just that little bit better.”

Trained by solar-power pioneers

Chen attended Sun Yat-sen University in Guangzhou, China, earning a bachelor’s degree in optics sciences and technologies in 2008. He stayed on to pursue a Ph.D. in photovoltaics sciences and technologies. His research was on high-efficiency solar cells made from wafer-based crystalline silicon. His adviser was Hui Shen, a leading PV professor and founder of the university’s Institute for Solar Energy Systems. Chen calls him “the first of three very important figures in my scientific career.”

In 2011 Chen spent a year as a Ph.D. student at Leibniz University Hannover, in Germany. There he studied under Pietro P. Altermatt, the second influential figure in his career.

Altermatt—a prominent silicon solar-cell expert who would later become principal scientist at Trina—advised Chen on his computational techniques for modeling and analyzing the behavior of 2D and 3D solar cells. The models play a key role in designing solar cells to optimize their output.

“Gaining 1 or 2 percent more efficiency is huge. These may sound like very tiny increases, but at scale, these small improvements create a lot of value in terms of economics, sustainability, and value to society.”

“Dr. Altermatt changed how I look at things,” Chen says. “In Germany, they really focus on device physics.”

After completing his Ph.D., Chen became a technical assistant at Trina, where he met the third highly influential person in his career: Pierre Verlinden, a pioneering photovoltaic researcher who was the company’s chief scientist.

At Trina, Chen quickly ascended through R&D roles. He has been the company’s assistant vice president of technology since 2023.

IEEE’s “treasure” trove of research

Chen joined IEEE as a student because he wanted to attend the IEEE Photovoltaic Specialists Conference, the longest-running event dedicated to photovoltaics, solar cells, and solar power.

The membership was particularly beneficial during his Ph.D. studies, he says, because he used the IEEE Xplore Digital Library to access archival papers.

“My work has certainly been inspired by papers I found via IEEE,” Chen says. “Plus, you end up clicking around and reading other work that isn’t related to your field but is so interesting.

“The publication repository is a treasure. It’s eye-opening to see what’s going on inside and outside of your industry, with new discoveries happening all the time.”

Diversity of thought in industrial design is crucial: If no one thinks to design a technology for multiple body types, people can get hurt. The invention of seat belts is an oft-cited example of this phenomenon, as they were designed based on crash dummies that had traditionally male proportions, reflecting the bodies of the team members working on them.

The same phenomenon is now at work in the field of motion-capture technology. Throughout history, scientists have endeavored to understand how the human body moves. But how do we define the human body? Decades ago many studies assessed “healthy male” subjects; others used surprising models like dismembered cadavers. Even now, some modern studies used in the design of fall-detection technology rely on methods like hiring stunt actors who pretend to fall.

Over time, a variety of flawed assumptions have become codified into standards for motion-capture data that’s being used to design some AI-based technologies. These flaws mean that AI-based applications may not be as safe for people who don’t fit a preconceived “typical” body type, according to new work recently published as a preprint and set to be presented at the Conference on Human Factors in Computing Systems in May.

“We dug into these so-called gold standards being used for all kinds of studies and designs, and many of them had errors or were focused on a very particular type of body,” says Abigail Jacobs, coauthor of the study and an assistant professor at the University of Michigan’s School of Information and the Center for the Study of Complex Systems. “We want engineers to be aware of how these social aspects become coded into the technical—hidden in mathematical models that seem objective or infrastructural.”

It’s an important moment for AI-based systems, Jacobs says, as we may still have time to catch and avoid potentially dangerous assumptions from being codified into applications informed by AI.

Motion-capture systems create representations of bodies by collecting data from sensors placed on the subjects, logging how these bodies move through space. These schematics become part of the tools that researchers use, such as open-source libraries of movement data and measurement systems that are meant to provide baseline standards for how human bodies move. Developers are increasingly using these baselines to build all manner of AI-based applications: fall-detection algorithms for smartwatches and other wearables, self-driving vehicles that need to detect pedestrians, computer-generated imagery for movies and video games, manufacturing equipment that interacts safely with human workers, and more.

“Many researchers don’t have access to advanced motion-capture labs to collect data, so we’re increasingly relying on benchmarks and standards to build new tech,” Jacobs says. “But when these benchmarks don’t include representations of all bodies, especially those people who are likely to be involved in real-world use cases—like elderly people who may fall—these standards can be quite flawed.”

She hopes we can learn from past mistakes, such as cameras that didn’t accurately capture all skin tones and seat belts and airbags that didn’t protect people of all shapes and sizes in car crashes.

The Cadaver in the Machine

Jacobs and her collaborators from Cornell University, Intel, and the University of Virginia performed a systematic literature review of 278 motion-capture-related studies. In most cases, they concluded, motion-capture systems captured the motion of “those who are male, white, ‘able-bodied,’ and of unremarkable weight.”

And sometimes these white male bodies were dead. In reviewing works dating back to the 1930s and running through three historical eras of motion-capture science, the researchers studied projects that were influential in how scientists of the time understood the movement of body segments. A seminal 1955 study funded by the Air Force, for example, used overwhelmingly white, male, and slender or athletic bodies to create the optimal cockpit based on pilots’ range of motion. That study also gathered data from eight dismembered cadavers.

A full 20 years later, a study prepared for the National Highway Traffic Safety Administration used similar methods: Six dismembered male cadavers were used to inform the design of impact-protection systems in vehicles.

In most of the 278 studies reviewed, motion-capture systems captured the motion of “those who are male, white, ‘able-bodied,’ and of unremarkable weight.”

Although those studies are many decades old, these assumptions became baked in over time. Jacobs and her colleagues found many examples of these outdated inferences being passed down to later studies and ultimately still influencing modern motion-capture studies.

“If you look at technical documents of a modern system in production, they’ll explain the ‘traditional baseline standards’ they’re using,” Jacobs says. “By digging through that, you quickly start hopping through time: OK, that’s based on this prior study, which is based on this one, which is based on this one, and eventually we’re back to the Air Force study designing cockpits with frozen cadavers.”

The components that underpin technological best practices are “man-made—intentional emphasis on man, rather than human—often preserving biases and inaccuracies from the past,” says Kasia Chmielinski, project lead of the Data Nutrition Project and a fellow at Stanford University’s Digital Civil Society Lab. “Thus historical errors often inform the ‘neutral’ basis of our present-day technological systems. This can lead to software and hardware that does not work equally for all populations, experiences, or purposes.”

These problems may hinder engineers who want to make things right, Chmielinski says. “Since many of these issues are baked into the foundational elements of the system, teams innovating today may not have quick recourse to address bias or error, even if they want to,” they say. “If you’re building an application that uses third-party sensors, and the sensors themselves have a bias in what they detect or do not detect, what is the appropriate recourse?”

Jacobs says that engineers must interrogate their sources of “ground truth” and confirm that the gold standards they measure against are, in fact, gold. Technicians must consider these social evaluations to be part of their jobs in order to design technologies for all.

“If you go in saying, ‘I know that human assumptions get built in and are often hidden or obscured,’ that will inform how you choose what’s in your dataset and how you report it in your work,” Jacobs says. “It’s sociotechnical, and technologists need that lens to be able to say: My system does what I say it does, and it doesn’t create undue harm.”