Stephen O'Driscoll
Assistant Professor, Electrical and Computer Engineering
University of California, Davis

office: Kemper Engineering Building (Engineering II) Room 2039 (map)
postal: Electrical and Computer Engineering, University of California, One Shields Avenue, Davis, CA 95616-5294
phone: 530-754-0435             fax: 530-752-8428
email:   url:

picture of Stephen 

Welcome to the homepage of Professor Stephen O'Driscoll of the Department of Electrical and Computer Engineering at the University of California, Davis.


My research interests are primarily in analog and RF circuit design for biomedical and other power-constrained applications with considerable overlap to system design, signal processing and electromagnetics.

My previous work ranges from 77GHz radar front ends, through Gigabit Ethernet clock and data recovery PLLs, to ultra low power ADCs and wireless power delivery for implantable medical devices. I see great opportunity for adaptive analog circuits, which vary their performance in response to dynamic system requirements, to deliver the efficiencies required for new ranges of therapeutic and diagnostic medical devices.

Some current and upcoming projects are outlined below:

Wireless Power Transfer

In-vivo monitoring and treatment of key biological parameters can greatly assist in managing health and preventing disease. Implantable medical devices are increasingly an essential healthcare tool. Powering these devices by means of batteries or inductive coupling limits the range of possible devices to geometricaly large devices placed just below the surface of the skin. Our work has enabled deeper implantation depth and smaller (x100) device size by developing a new wireless power transfer regime. Currently we are extending this technology to enable even greater implant depths, integrating the receiving antenna on-chip to deliver more robust devices, and developing technology for distributed implanted devices.

Implant Positioning System (IPS)

Knowledge of the precise position of implanted devices enables measurements taken by the device to be correctly interpreted and greatly simplifies post-implantation manipulation of the device (e.g. when injecting refills into the reservoir of implanted drug delivery pumps precise knowledge of the position allows the refill to be delivered in one shot, reducing patient discomfort and the probability of infection). When we deliver power to implanted devices a current is induced in the receiving antenna which generates a weak electromagnetic field, the "scattered field". The Implant Positioning System deduces the implant location from spatial variations in this scattered field. This work poses a number of unique challenges in analog and RF circuit design, electromagnetic analysis, and signal processing.

Neural Interfaces

High-performance prosthetic systems for humans will require sensing and control of tens of thousands of neural channels, one hundred times more channels than the systems we have previously developed for smaller primates. Delivering such performance within the power constraints of implanted devices is a major challenge. We are investigating approaches to reduce power consumption in neural signal acquisition and neural actuation circuits through a combination of adaptive circuit design and signal processing.

Smart Pill

An ingestable smart capsule to allow in-vivo continuous monitoring of key physiological parameters is under investigation.

Analog Optimization

Analog design cost faces twin challenges in increasing design time with technology development and stagnation in architectural innovation. Consequently there is a clear need for effective analog circuit optimization, synthesis and automated design tools. There has been some work on analog optimization but such tools are mostly heuristic based and thus viable only for small circuit blocks. We are combining circuit design and sate-of-the-art optimization knowledge to solve circuit challenges using efficient, deterministic optimization techniques such as convex optimization. Convex optimization has been applied to digital design by companies and we have already successfully used convex optimization to develop an analog equalizer for 6.25GB/s 4-PAM backplane communications.

Genetic Analog

We are developing a framework for self-designing circuits using hardware evolution to unleash the full power of a given IC beyond the constraints of modeling and the existing library of analog architectures.



I will teach EEC110A Electronic Circuits I in Winter 2010 and EEC289O Medical IC Design in Spring 2010.

I have taught EE114X Simulation Based Circuit Design in the fall quarters of 2006, 2007 and 2008 at Stanford University.



  • Ph.D., Electrical Engineering, Stanford University, June 2009
    Research Advisor: Professor Teresa H. Meng
    Dissertation: Adaptive Signal Acquisition and Power Delivery for Implantable Medical Devices
  • M.S., Electrical Engineering, Stanford University, March 2005
  • B.E., Electrical and Microelectronic Engineering, University College Cork, Ireland, June 2001


  • Stanford Graduate Fellowship, Lu Fellow 2003-2009
  • IEE Prize for Best EE Graduate in Ireland 2001.
  • SCI Ireland Scholarship 2001.
  • Motorola Fellowship (Ireland) 2000.
  • University College Cork Engineering Scholarship 1997-2001.
  • Irish Math Team 1997 International Mathematical Olympiad 1997, Argentina

  • Industrial Experience

  • Oct 2001 - Aug 2003, Design Engineer, Data Communications Division, Cypress Semiconductor, San José, California
  • June 1999 - Sept 1999 and May 2000 - Sept 2000, Student Engineer, Farran Technology, Ballincollig, Co. Cork, Ireland
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    › Biomedical Electronics
      › Wireless Power Transfer
      › IPS
      › Neural Interfaces
      › Smart Pill
    › Analog Design Automation
      › Analog Optimization
      › Genetic Analog