Agenda

Microelectronics Colloquium

Tenure track colloquium

Sten Vollebregt, Massimo Mastrangeli, Daniele Cavallo

Wideband phased arrays for future wireless communication terminals, Daniele Cavallo (TS group)

Wireless data traffic will grow exponentially in the next years, due to the proliferation of user terminals and bandwidth-greedy applications. To address this demand, the next generations of mobile communication (5G and beyond) will have to shift the operation to higher frequencies, especially to millimetre-wave (mmWave) spectrum (30-300 GHz), that can provide extremely high-speed data links. To enable mm-wave wireless communication, mobile terminals such as smartphones will need phased arrays antennas, able to radiate or receive greater power in specific directions that can be dynamically steered electronically. However, to cover the different 5G mm-wave bands simultaneously (28, 39, 60 GHz, …) and to achieve total angular coverage, too many of such antennas should be on the same device: the main bottleneck is the insufficient space available to place all antenna modules. Therefore, I propose to investigate novel phased array antenna solutions with very large angular coverage and ultra-wide frequency bandwidth, to massively reduce the overall space occupation of handset antennas and overcome the current limitations of mobile terminal antenna development.

Towards smart organs-on-chip, Massimo Mastrangeli (ECTM Group)

Organs-on-chip are microfluidic systems that enable dynamic tissue co-cultures under physiologically realistic conditions. OOCs are helping innovating the drug screening process and gaining new fundamental insights in human physiology. In this talk, after a summary of my past research journey, I will describe how the ECTM group at TU Delft is envisioning the use microfabrication and materials science to embed real-time sensing and actuation in innovative and scalable OOC platforms.

Emerging electronic materials: from lab to fab, Sten Vollebregt (ECTM group)

Due to their nm-size features and often unique physical properties nanomaterials, like nanotubes and 2D materials, can potentially outperform classical materials or even provide functionality which cannot be obtained otherwise. Because of this, these nanomaterials hold many promises for the next generation of devices for sensing & communication and health & wellbeing.

Unfortunately, many promising applications of nanomaterials never reach sufficient maturity to be implemented in actual products. This is mostly because the interest in the academic community reduces once the initial properties have been demonstrated, while the risk for industrialization is still too high for most companies to start their own R&D activities. My goal is to bridge these two worlds by investigating the integration of novel nanomaterials in semiconductor technology and demonstrating the scalability of novel sensing devices. In this talk, I will give examples on how carbon nanotubes, graphene and other emerging nanomaterials can be used in the next generation of sensing devices.

Overview of Microelectronics Colloquium

Agenda

Microelectronics Colloquium

Sten Vollebregt, Massimo Mastrangeli, Daniele Cavallo

Tenure track colloquium

Daniele Cavallo (TS group); wideband phased arrays for future wireless communication terminals, Massimo Mastrangeli (ECTM Group); Towards smart organs-on-chip, Sten Vollebregt (ECTM group) Emerging electronic materials: from lab to fab

Signal Processing Seminar

Krishnaprasad Nambur Ramamohan

Signal processing algorithms for acoustic vector sensors

Symposium MRI for Low-Resource Setting

Steven Schiff, Johnes Obungoloch

Sustainable Low-Field MRI Technology for Point of Care Diagnostics in Low-Income Countries

Kick-off meeting of the project "A sustainable MRI system to diagnose hydrocephalus in Uganda"

Signal Processing Seminar

Peter Gerstoft

Machine learning in physical sciences

Machine learning (ML) is booming thanks to efforts promoted by Google. However, ML also has use in physical sciences. I start with a general overview of ML for supervised/unsupervised learning. Then I will focus on my applications of ML in array processing in seismology and ocean acoustics. This will include source localization using neural networks or graph processing. Final example is using ML-based tomography to obtain high-resolution subsurface geophysical structure in Long Beach, CA, from seismic noise recorded on a 5200-element array. This method exploits the dense sampling obtained by ambient noise processing on large arrays by learning a dictionary of local, or small-scale, geophysical features directly from the data.

Signal Processing Seminar

Aydin Rajabzadeh

manufacturing defect detection