At this year's LASER World of PHOTONICS in Munich, Physik Instrumente (PI) will present its current research and development topics. These will include the precise shaping of surfaces using intelligent actuator and drive concepts, the quick, accurate, and particle-free movement of workpieces, and the optimization of system performance with the help of machine learning. Hence, the specialist for nano positioning, piezo technology, and performance automation is giving an outlook on future developments.
Worldwide, around seventy PI engineers, physicists, and software specialists are working on innovative technologies for precision positioning. Five forward-looking topics will be presented for the first time at LASER World of PHOTONICS. "By presenting these topics at this time, we are inviting our customers to get involved in a dialog, in order to discuss ideas and requirements for future generations of technology, and identify potential applications," explains Dr. Markus Czanta, PI's CTO.
Active Damping and Vibration Compensation
To improve exploiting the potential of high-precision positioning technologies, it is necessary to damp or compensate vibrations in mechanical setups. A smart actuator and drive concept for active damping and vibration compensation will be presented with the aim of developing solutions for demanding scientific and industrial applications.
Magnetic Levitation
Magnetic levitation deals with contactless stages supported solely by magnetic fields and freely positionable in space in six degrees of freedom. In combination with special drive and sensor technologies, this technology should enable resolutions down to the picometer range with high dynamics and without generating particles.
Surface Shaping
Another focus of research is the high-resolution active shaping of optical component or substrate surfaces. Combining different actuator designs, modes of operation, and arrangements enables amplitudes and deformations with smallest feature sizes and high dynamics.
Learning Algorithms - Machine Learning
How is it possible to overcome the limitations of feedback and feedforward-based control? PI's Israeli subsidiary ACS is focusing its research about controllers on learning algorithms. Using machine learning, the controller software should independently transfer knowledge from all previous motions to future tasks, therefore preventing known errors.
New Controller Architecture
Developers will also report on the status of PI’s future controller architecture. This will cover various drive and sensor technologies, software solutions, operating modes, and communication standards. PI engineers are paying particular attention to customizing the architecture to meet individual application needs, rapid commissioning, and programming.