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Division of Electronics and Embedded Systems

The division has a research and educational profile that covers electronics from a very broad range of basic topics including semiconductor device technology and circuits to systems integration and embedded systems implementation in industrial applications oriented to industry automation and medical electronics. Several application areas also cover internet of everything, interaction with e-science, electrical power systems, microsystems technology and brain-like computing.

Research areas

Device and Circuit Fabrication

Semiconductor devices are the keys to building any electronic circuit. Our research ranges from nanometer scaled silicon and germanium MOSFETs to high voltage silicon carbide (SiC) devices and photonic devices like lasers and UV-detectors.

Embedded Systems

Research of vital functions in areas like transport, communication or health care. Embedded computer systems are integrated into a physical or electrical environment and react continuously with this environment and have to satisfy different requirements, such as real-time, power, size or costs.

Emerging Technologies

Research in emerging devices in printed and flexible electronics, integrated photonics, quantum communication and spintronic devices and circuits. Neural network accelerators and neuromorphic brain-like computing are investigated as techniques useful for machine-learning.

Integrated Circuits and Systems

Research focuses on analog/mixed signal ASICs covering a wide range of applications. The department has a track record in the area of fundamental innovations in VLSI/SoC architecture like Network on Chip, Clocking and Power Management, testing, security and high-performance massively parallel architectures. 

Meet the division

Recent publications

[1]
W. Zhu, Y. Chen and Z. Lu, "Pooling On-the-Go for NoC-Based Convolutional Neural Network Accelerator," in Embedded Computer Systems: Architectures, Modeling, and Simulation - 24th International Conference, SAMOS 2024, Proceedings, 2025, pp. 109-118.
[2]
W. Zhu, "Efficient Machine Learning for Edge Computing : Architecture and Application," Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2025:26, 2025.
[3]
Y. Chen, W. Zhu and Z. Lu, "Travel Time-Based Task Mapping for NoC-Based DNN Accelerator," in Embedded Computer Systems: Architectures, Modeling, and Simulation - 24th International Conference, SAMOS 2024, Proceedings, 2025, pp. 76-92.
Full list in the KTH publications portal

News

Space. Photo: Jeremy Thomas, Unsplashed

The mysteries of space - the theme of Space Rendezvous in Kista

Once again this year, KTH Space Centre, together with Smartare Elektroniksystem and Visit Kista, invites you to Space Rendezvous in Kista. This year's theme is the mysteries of space - from comets to ...

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Research wants to change fetal monitoring at birth

Researchers at KTH and KI want to reduce the risk of organ failure in children during labour. Therefore, they have started a programme, "Continuous lactate measurement and preventive hypoxia during de...

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Research behind the efficiency of mobile phone networks - one of the most highly cited

The research behind using mobile phone masts with many small electrically steerable antennas has been included in Clarivate's annual Clarivate Highly Cited Researchers 2023 list. ”This is of cours...

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