Christian Paintz, Melexis
“Particularly in the evaluation of measurement data, IMMS has impressively demonstrated that a learning algorithm is on a par with manual evaluation – while saving a great deal of time. We are also continuing to pursue methods for circuit and layout analysis, as we see great research and application potential here as well.”
“At our site in Erfurt, we develop and produce highly integrated circuits mainly for the automotive industry, including for example the driver for ambient light LEDs, motor controller but also sensors.
In the process, our chips are constantly faced with the challenge of being safe and robust while integrating more and more functions at the same time. On the basis of this, it is necessary to comprehensively analyse and test the circuits. This generates considerable amounts of data that have to be evaluated in detail. On the other hand, due to the smaller silicon structures and the higher integration density associated with them, for example unwanted interference caused by crosstalk between neighbouring cells in the layout becomes more critical and must be identified and reduced to a tolerable level.
The IntelligEnt research group, of which I was privileged to be the speaker, addresses this by using machine learning methods early in the design and characterisation process. This approach promises to integrate empirical knowledge into new methods and thus make our chips better through elegant structures and procedures.
Particularly in the evaluation of measurement data, IMMS has impressively demonstrated that a learning algorithm is on a par with manual evaluation – while saving a great deal of time. We are also continuing to pursue methods for circuit and layout analysis, as we see great research and application potential here as well. In the near future, methods for automatically mapping the power consumption of our chips will also help us to increase energy efficiency.”
The reference is related to:
Core topic
AI-based design and test automation
We are researching to use AI to make the development process of integrated sensor systems safer and more cost-effective. AI can help developers in the process to avoid errors and apply informal knowledge in an automated way.
Service for R&D
IC design methods
We develop new AI-based methods and tools for your system-on-chip and FPGA designs to master the increasing complexity of integrated systems and thus further increase performance.
Project
IntelligEnt
The IMMS researched assistance systems for chip designers: Machine Learning improves design and test methods for integrated analogue/mixed-signal systems.
Press release
Reliable and faster chip designs through invasive and parametric simulation methods
Dissertation on new methods for automation in integrated circuit design
Press release
EDA Competition Award for "Trash or Treasure" - Intelligent Layout Processing
1st prize of the IEEE CEDA goes to young scientists from Ilmenau TU and IMMS
Contact
Contact
Eric Schäfer, M. Sc.
Head of Microelectronics / Branch Office Erfurt
eric.schaefer(at)imms.de+49 (0) 361 663 25 35
Eric Schäfer and his team research Integrated sensor systems, especially CMOS-based biosensors, ULP sensor systems and AI-based design and test automation. The results are being incorporated into research on the lead applications Sensor systems for in-vitro diagnostics and RFID sensor technology. It will assist you with services for the development of Integrated circuits and with IC design methods.