RFID Reader Design for Neural Implants

Christopher Sutardja

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2018-153
December 1, 2018

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-153.pdf

Brain machine interfaces (BMI) have the ability to revolutionize both healthcare and neuroscience. Recent advances in neural interfaces have demonstrated the ability to control prosthetic limbs and to monitor and treat neurological diseases. Implants with the ability to acquire neural signals are the primary sources of information used to control these systems. To ensure low risk of infection, these implants should be powered wirelessly and communicate wirelessly to the outside world. An efficient, wearable RFID interrogator, sitting outside the skull delivering power to and receiving uplink data from these implants, is a critical component of a wireless BMI system that is convenient and safe for patients to use. However, RFID readers have a classical problem of self-interference from the transmitted carrier leaking into the reader’s receive path, potentially saturating and desensitizing the RX amplifiers. Conventional methods of carrier rejection result in an RFID reader form factor that is too bulky and impractical to be worn on a human head. To solve these problems, we have developed a novel architecture for a fully integrated RFID transceiver that uses only 1 antenna and no bulky isolation components. We exploit the non-linearity of a class E/Fodd switching PA and use it as a demodulator to mix our received backscattered signal back to baseband. The reader is fabricated on a 1.2 mm x 1.5 mm CMOS Integrated Circuit and was tested with a proprietary implant through a channel of pig skin and bovine t-bone to mimic the channel characteristics of a human skull. The system achieves a data rate of 2 Mb/s, while delivering 790 uW of power to the implant at a TX power consumption of 35 mW for an overall link efficiency of 2.3%. This efficient reader is well-suited for BMI systems meant for long-term chronic neural recording for the purposes of monitoring neurological diseases such as epilepsy and Alzheimer’s disease.

Advisor: Jan M. Rabaey


BibTeX citation:

@phdthesis{Sutardja:EECS-2018-153,
    Author = {Sutardja, Christopher},
    Title = {RFID Reader Design for Neural Implants},
    School = {EECS Department, University of California, Berkeley},
    Year = {2018},
    Month = {Dec},
    URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-153.html},
    Number = {UCB/EECS-2018-153},
    Abstract = {Brain machine interfaces (BMI) have the ability to revolutionize both healthcare and neuroscience. Recent advances in neural interfaces have demonstrated the ability to control prosthetic limbs and to monitor and treat neurological diseases. Implants with the ability to acquire neural signals are the primary sources of information used to control these systems. To ensure low risk of infection, these implants should be powered wirelessly and communicate wirelessly to the outside world. An efficient, wearable RFID interrogator, sitting outside the skull delivering power to and receiving uplink data from these implants, is a critical component of a wireless BMI system that is convenient and safe for patients to use.
However, RFID readers have a classical problem of self-interference from the transmitted carrier leaking into the reader’s receive path, potentially saturating and desensitizing the RX amplifiers. Conventional methods of carrier rejection result in an RFID reader form factor that is too bulky and impractical to be worn on a human head. To solve these problems, we have developed a novel architecture for a fully integrated RFID transceiver that uses only 1 antenna and no bulky isolation components. We exploit the non-linearity of a class E/Fodd switching PA and use it as a demodulator to mix our received backscattered signal back to baseband.  The reader is fabricated on a 1.2 mm x 1.5 mm CMOS Integrated Circuit and was tested with a proprietary implant through a channel of pig skin and bovine t-bone to mimic the channel characteristics of a human skull. The system achieves a data rate of 2 Mb/s, while delivering 790 uW of power to the implant at a TX power consumption of 35 mW for an overall link efficiency of 2.3%. This efficient reader is well-suited for BMI systems meant for long-term chronic neural recording for the purposes of monitoring neurological diseases such as epilepsy and Alzheimer’s disease.}
}

EndNote citation:

%0 Thesis
%A Sutardja, Christopher
%T RFID Reader Design for Neural Implants
%I EECS Department, University of California, Berkeley
%D 2018
%8 December 1
%@ UCB/EECS-2018-153
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-153.html
%F Sutardja:EECS-2018-153