IMMS develops novel designer assistance system and smart models to design sensor and control chips for I4.0 applications
The aim of KI-EDA is to harness the tools of artificial intelligence (AI) for the rapid and reliable design of customised encoder and sensor chips. Such microelectronic systems are needed for novel intelligent, autonomous production systems that can securely analyse data in a decentralised manner to predict and improve manufacturing scenarios.
Conventional design methods are usually not sufficient for complex and individual designs
Encoder and sensor chips are used, for example, to detect rotary movements on machines and convert them into electronic signals for digital processing. These chips are customised for production machinery and processes and are becoming increasingly powerful and complex. The design and verification of the chips is reaching its limits with conventional electronic design automation (EDA) methods and is largely shaped by the experiential knowledge of design engineers.
New methods should reduce the development time of customised chips by up to two thirds
This is why the KI-EDA project, which is being led by iC-Haus GmbH, is working on AI-supported methods of design automation and on a modular system for chip design. Individual functions should be able to be selected, combined and simulated quickly and cost-effectively in the form of function blocks with short design times and a low error rate, and transferred into an encoder or sensor chip customised for the planned application. Furthermore, this makes it possible to implement new functionalities such as AI-based predictive maintenance solutions flexibly and quickly. Errors in the design and thus time-consuming redesigns are to be reduced in this way. The aim is to reduce the development time of customised chips by up to two thirds.
To this end, the partners iC-Haus and CENTITECH will research and implement intelligent functions and energy-autonomous solutions for the new microelectronic systems and characterise them in application-oriented demonstrators. This is intended to ensure the properties required for widespread and comprehensive use in I4.0 production environments. In addition, the partners will ensure that the systems can be manufactured in high quantities.
IMMS transfers ML algorithms and smart models into modular system for fast and safe chip design
IMMS will research machine learning (ML) algorithms for their applicability in chip design (EDA) and transfer them into a novel designer assistance system to make design processes more efficient and faster. The new ML methods to be researched will primarily serve a fast design validation and thus increase design security. They are aimed in particular at model-based debugging and should reduce the complexity of working with IP libraries, i.e. with collections of existing circuit components or electronic blocks. New chips can thus be assembled in a modular system. In addition, IMMS will research and implement „smart models“. For the first time, new algorithms will enable models to computationally check their own range of validity and thus exclude false-positive verification results.
Acronym / Name:KI-EDA / Modular system with artificial intelligence for the accelerated development of special chips for Industry 4.0
Duration:2020 – 2022
Application:Automation technology and Industry 4.0|EDA| Electronic Design Automation| Automation Chip Design| ASIC Design| Verification
Research field:Integrated sensor systems
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.
The KI-EDA project is funded by the Federal Ministry of Education and Research within the framework of the programme ”Microelectronics for Industry 4.0 (ElektroniK I4.0)“ under the consortium number es2eli4001, IMMS under the reference 16ME0010.