AI semiconductors are facing various issues such as consuming enormous power and causing environmental problems. To solve numerous AI semiconductor issues, our team focuses on design of well-optimized intelligent semiconductor devices and circuits.

Our team is conducting intelligent semiconductor research by "using" Artificial Intelligence technology. Each design stage is converted into data using an EDA tools such as TCAD or SPICE, and deep learning is used to find the optimal semiconductor design point and verify the effectiveness of the application of the semiconductor field such as artificial intelligence robots and autonomous vehicles.

Our team is working on intelligent semiconductors "for" Artificial Intelligence applications. Artificial intelligence semiconductors require high performance, energy efficiency, and memory integration technology. We are working hard to improve high-speed processing power, reliability, and durability in order to secure high performance, and ultra-low power design and radiation-resistant design to improve energy efficiency. These efforts to improve energy efficiency also lead to efforts to improve environmental problems.

AI semiconductors are currently focusing on memory integration technology to solve data bottlenecks and memory access speed problems, and are also researching new memory integration technologies that can solve these problems because integrating memory and logic causes reliability, thermal problems, and mechanical defects.