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Your search keyword '"Hannes Dahlberg"' showing total 5 results
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5 results on '"Hannes Dahlberg"'

1. Memory State Dynamics in BEOL FeFETs: Impact of Area Ratio on Analog Write Mechanisms and Charging

Author

Hannes Dahlberg, Oscar Kaatranen, Karl-Magnus Persson, Arto Rantala, Jacek Flak, and Lars-Erik Wernersson

Subjects
BEOL integration, CMOS, FeFET, ferroelectricity, HZO, non-volatile memory, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

This work presents dynamic state writing by combining ferroelectric (FE) polarization together with charge injection (CI) on Si-based ferroelectric MOSFETs as a novel approach for non-volatile memory design. FE capacitors are non-destructively integrated in the Back-End-of-Line (BEOL) with Si MOSFETs to create FE-Metal-FETs (FeMFETs). We explore the FE/MOS area ratio (AR) as a critical design parameter, particularly in the context of dynamic writing processes, where various voltage pulse trains are applied for analog potentiation and depression of the memory state. AR significantly influences both the electric field distribution over the FE and the extent of CI from the top electrode. Constant-pulse writing schemes enable analog threshold voltage modulation by considering the AR, with reduced voltages and faster operation for smaller ARs. Retention of intermittent states written by FE polarization combined with CI is demonstrated, illustrating the stability and effectiveness of FeMFET devices and AR optimization for memory applications.

Published
2025
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5. Pulsed Millimeter Wave Radar for Hand Gesture Sensing and Classification

Author

Lars-Erik Wernersson, Sebastian Heunisch, Hannes Dahlberg, Lars Ohlsson Fhager, and Anton Evertsson

Subjects
Artificial neural network, Computer science, business.industry, 010401 analytical chemistry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Confusion matrix, 020206 networking & telecommunications, 02 engineering and technology, Frame rate, 01 natural sciences, Convolutional neural network, 0104 chemical sciences, law.invention, Gesture recognition, law, 0202 electrical engineering, electronic engineering, information engineering, Computer vision, Artificial intelligence, Electrical and Electronic Engineering, Radar, Envelope (radar), business, Instrumentation, Gesture
Abstract

A pulsed millimeter wave radar operating at a frame rate of 144 Hz is utilized to record 2160 scattering signatures of 12 generic hand gestures. Gesture recognition is achieved by machine learning, utilizing transfer learning on a pretrained convolutional neural network. This yields excellent classification results with a validation accuracy of 99.5%, based on a 60% training versus 40% validation split. The corresponding confusion matrix is also presented, showing a high level of classification orthogonality between the tested gestures. This is the first demonstration where data from a pulsed millimeter wave radar is used for gesture recognition by machine learning. It proves that the range-time envelope representation of high frame-rate data from a pulsed radar is suitable for hand gesture recognition. Further improvements are expected for more complex detection schemes and tailored neural networks.

Published
2019
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