Traditional high-power lasers are bulky and the equipment required makes them difficult to use on portable platforms. But engineers from the University of Texas at Arlington are working hard to develop a semiconductor laser pointer that solves these problems. A professor at the Department of Electronic Engineering at the University of Texas at Arlington, as a five-year, $ 3 million multidisciplinary research project approved by the High-Energy Laser Joint Technology Office, he will develop small, efficient, and scalable power A semiconductor laser.
Currently working with the University of New Mexico, Stanford University and the US Air Force Research Laboratory. Semiconductor lasers use cavities to generate feedback, reflect light back and forth, and eventually emit from the surface of the laser. Semiconductor lasers have become an important tool to promote the next generation of innovative technologies, and revenue is expected to exceed US $ 10 billion in 2016.
However, while maintaining excellent beam quality, high energy efficiency and compact size, there are still many challenges to expand the laser power to the kilowatt and megawatt level. Zhou Weidong and his team will rely on nanotechnology principles and structures to achieve high-power lasers with scalable power.
Zhou Weidong said: “We have been studying all aspects of lasers, such as high-power infrared lasers in military, manufacturing and security fields, which are particularly important; and how to deal with the main challenges related to these applications. If we are creating semiconductors based on nanotechnology The success of lasers will greatly reduce the size of these lasers while increasing their power and efficiency. ”
Finally, the green laser pointer can be built on a semiconductor wafer. The control electronics and packages required to operate the laser can be reduced in size to a computer-level size that is much smaller than current laser operation packages. The application prospects of these lasers may include precision laser cutting and 3D processing that can be performed on desktop machines, and lidar sensing of autonomous vehicles. In addition, smaller platforms will also be more cost-effective.