Engineers develop a strategy to streamline photo voltaic cell testing, accelerating a course of that may be sluggish and dear – TechnoNews

The method of testing new photo voltaic cell applied sciences has historically been sluggish and dear, requiring a number of steps. Led by a fifth-year Ph.D. pupil, a Johns Hopkins crew has developed a machine studying technique that guarantees to dramatically pace up this course of, paving the way in which for extra environment friendly and inexpensive renewable vitality options.

“Our work shows that machine learning can streamline the solar cell testing process,” mentioned crew chief Kevin Lee, who labored with fellow electrical and laptop engineering graduate college students Arlene Chiu, Yida Lin, Sreyas Chintapalli, and Serene Kamal, and undergraduate Eric Ji, on the mission. “This not only saves time and resources but opens new possibilities for clean energy technology development.”

The crew’s outcomes seem in Superior Clever Methods.

A serious hurdle in commercializing new photo voltaic supplies and gadgets is the prolonged fabrication-testing-iteration cycle. Optimizing a brand new photo voltaic cell materials for the market is an arduous course of. After a tool is made, a number of time-consuming measurements are wanted to grasp its materials properties. This information is then used to regulate the fabrication course of, repeating the cycle.

The brand new technique drastically reduces this time by extracting all of the supplies’ vital traits from a single measurement. Not like different strategies skilled on computer-simulated information—which regularly produce inaccurate outcomes—the Hopkins crew’s method makes use of real-world information.

Their neural community collects hundreds of knowledge factors from one photo voltaic cell, capturing advanced properties and variations attributable to defects, akin to spin-casting streaks, cracks, and contaminants, and eliminating the necessity to fabricate hundreds of photo voltaic cells.

“Kevin’s method has the potential to speed up photovoltaic development times,” mentioned Lee’s adviser and examine co-author Susanna Thon, an affiliate professor {of electrical} and laptop engineering at JHU’s Whiting College of Engineering and affiliate director of the college’s Ralph O’Connor Sustainable Vitality Institute.

“Instead of laboriously making multiple measurements on many devices to learn what you need to know about device behavior, Kevin, thanks to his [machine learning] algorithm, can now tell you everything you’d want to know about a device and its properties from a single measurement that takes about 30 seconds.”

The opposite novel characteristic of Lee’s system is that it takes spatial maps of knowledge from photo voltaic cells and converts them into photos.

“Normally one of the most common measurements you get after creating a new solar cell is called a JV curve, and what it does is measure the cell’s response to light,” Lee mentioned.

“We had the idea of converting these JV curve maps into images so we could take advantage of advanced machine learning models developed for applications not in materials science, but in computer vision, to learn patterns in solar cell behavior.”

One other advantage of the brand new technique is its applicability to numerous supplies and gadgets past photo voltaic cells, probably accelerating the timeline from materials discovery to market adoption.

“In theory, the system we developed could be used to measure other devices, such as transistors and light sensors,” Lee mentioned. “The time saved, and the accuracy of this system could lead to a wide array of new technologies being created much more quickly, which I am excited to see happen.”