- Control and optimization
- Semiconductor
Chip Placement with RL
Developing chip simulation environments and placement algorithms to train RL models that optimize element placement.
READ MORE
Recent robotics-automated manufacturing process requires not only assigning tasks but also planning motions among multiple robot machines. Until recent days, this kind of works have been done by human experts. In general, it takes a lot of trial-and-errors and verification processes to manually create a program that accomplish the objective tasks while avoiding collision and interference from each other robot machine.
We use domain knowledge to create an objective function that satisfies the constraints of the actual process and also employ various ML and optimization techniques to automatically assign each task to the robot. Path planning is carried out in accordance with the assigned task in order to create a fast and safe robot program. Additionally the algorithms implemented as modules support parallel executions in order to improve the speed and efficiency of route search.
The automatically generated robot program effectively performs the objective processes in a assembly line comprised of multiple robots. Work that used to take months is now completed in a week.
Want to learn more about this use case?
Get in touch with our industrial AI experts.
Developing chip simulation environments and placement algorithms to train RL models that optimize element placement.
READ MORE
Using an unsupervised learning algorithm, it detects those anomalies before they occur in the equipment.
READ MORE
Reinforcement learning is used to reduce the time required for SMT procedures by making electronic components more efficiently installed on PCBs.
READ MORE
