Michel M. Maharbiz (Advisor)

Research Advised by Professor Michel M. Maharbiz

BPN871: An Ultrasonic Implantable for Continuous In Vivo Monitoring of Tissue Oxygenation

Soner Sonmezoglu
2022

Our group previously demonstrated a “neural dust” system for neural recording which includes an implantable device and external ultrasonic transducers to power and communicate with the implantable. In this work, we extend that paradigm, demonstrating an implantable that can measure and report tissue oxygenation. Oxygenation state is a key parameter when assessing the metabolic state of cells and tissues, tissue and organ viability, tumor state, among many examples in both basic science and clinical care. Various types of methods for the detection of oxygen have appeared in recent...

Soner Sonmezoglu

Alumni
Electrical Engineering and Computer Sciences
Professor Michel M. Maharbiz (Advisor)
Ph.D. 2017, PostDoc 2022

Soner received the B.S. and M.S. degrees (with high honors) in Electrical and Electronics Engineering from Middle East Technical University (METU), Ankara, Turkey, in 2010 and 2012, respectively. He received his Ph.D. degree in Electrical Engineering from the University of California, Davis, in 2017. During his Ph.D., he worked on micromachined inertial sensors and their interface electronics.

He is a postdoctoral researcher in Electrical Engineering and Computer Sciences department at the University of California, Berkeley working with Prof. Michel M. Maharbiz. Soner’s current...

BPN890: Hydrogel Actuated Carbon Fiber Microelectrode Array

Oliver Chen
2022

Glial scarring and passivization of long-term implanted neural probes is one bottleneck in brain- machine interface technology. However, ultraflexible probes with similar mechanical properties as tissue have been shown to minimize scarring and other biological responses. We propose a flexible, microscale neural probe that can be actuated using an expanding hydrogel. This device is designed to be able to record neural signals up to hundreds of microns away from the insertion site. This design can allow for high-density, accurate neural recordings for a wide variety of clinical applications...

BPN848: Wireless Neural Sensors: Robust Ultrasonic Backscatter Communication in the Brain

David Piech
2021

Brain-machine interfaces provide an artificial conduit to send information to and from the brain, and modulate activity in the brain. These systems have shown great promise in clinical, scientific, and human-computer interaction contexts, but the low reward/risk ratio of today’s invasive neural interfaces has limited their use to an extremely niche clinical patient population. It has been shown that ultrasonic backscatter communication can enable the sensing and stimulation of neural activity with extremely small wireless implants, which can both improve performance and reduce risk....

David Piech

Alumni
Electrical Engineering and Computer Sciences
Professor Michel M. Maharbiz (Advisor)
Ph.D. 2021

David’s research activities focus on neural interface devices and brain-machine interface systems, with the goal of enabling wider adoption of these technologies through vastly reduced-risk in-situ neural recording and stimulation modalities.


Previously, he was a research engineer at a private invention lab and tech incubator where he contributed to research in metamaterials-based antennas (spun out as Echodyne, Inc). In addition, he led and worked on projects in close collaboration with the Bill & Melinda Gates Foundation, including a microfluidic tool to aid in malaria...

Kyoungtae Lee

Alumni
Electrical Engineering and Computer Sciences
Professor Michel M. Maharbiz (Advisor)
Ph.D. 2021

Kyoungtae received his B.S. degree in Electrical Engineering from Korea Advanced Institute of Science and Technology (KAIST) in 2011 and his M.S. degree from the University of Texas at Austin in 2013, where he worked on designing continuous time delta sigma ADCs. He worked at KAIST IT convergence from 2013 to 2016, designing RF modules for future 5G cellular communication. His current research includes ASIC design for implantable bio-sensor, especially for cancer detection.

Electronic Interfaces for Bacteria-Based Biosensing

Tom Zajdel
Michel M. Maharbiz
2018

Bacterial sensing systems have evolved to detect complex biomolecules, operating near fundamental physical limits for biosensing. No modern engineered biosensor has managed to match the efficiency of bacterial systems, which optimize for each sensing application under constraints on response time and sensitivity. An emerging approach to address this short fallis to build biosensors that electronically couple microbes and devices to combine the sensing capabilities of bacteria with the communication and data processing...

Charge Pumping with Human Capacitance for Body Energy Harvesting

Alyssa Y. Zhou
Michel M. Maharbiz
2020

The proliferation of Internet-of-Things (IoT) systems and human body sensors is rapidly transforming the way we interact with our surroundings. As these devices increase in number and longevity, there grows a critical need to find sustainable and convenient power sources. Shrinking consumer electronics have generated a demand for battery-less power sources forsome applications. Significant interest in studying energy harvesting techniques exists as a solution to power these devices. In particular for interactive...

BPN922: Analog Optical Voltage Sensor

Jordan L. Edmunds
Soner Sonmezoglu
2021

Distributed sensors are becoming ubiquitous in manufacturing, automotive, and consumer applications. One extremely common need at the core of many of these sensors is the requirement to sense small voltages (uV-mV scale), amplify, digitize, and then communicate those bits so they can be acted on. We are taking a different approach - by utilizing nonlinear optical materials, we plan to transduce these signals directly into reflected light, removing the need for complex and high-cost sensor-side circuitry. Since the mechanism is purely passive and does not require a continuous power...