The ultimate limits of energy-efficient electronics and energy-harvesting systems that power them are not set by Moore’s law scaling of digital electronics but by the fundamental limits of physics for analog sensing, actuating, and communicating systems. These include thermal noise, which is power invariant with technology, though its area can scale favorably with technology [1]. Furthermore, digitally programmable analog architectures can enable energy-efficient design well beyond Moore’s law [1, 8]. For implantable electronics, such as are needed in neural implants, ultra-low-power bioelectronics that are either highly energy efficient or already near the fundamental limits of physics have been developed for nerve recording [2], nerve stimulation [3], nerve decoding [4], and wireless power and communication [5, 6]. Such systems can enable ultra low power bioelectronics to be powered by harvesting glucose in the cerebrospinal fluid [1, 7].

We describe how we can leverage well-known silicon fabrication techniques to create biocompatible abiotic glucose fuel cells, suitable for powering ultra-low-power bioelectronics on a silicon wafer. The advantage of such techniques is that the bioelectronics and the energy-harvesting solution may both be fabricated on the same silicon wafer with well-evolved semiconductor manufacturing techniques. Thus, compact and easily manufactured solutions for medical devices become feasible. 

Such fuel cells require a Raney-catalyst platinum anode, a carbon-nanotube cathode, and a Nafion-based polymer-electrolyte membrane [7]. The patterning and creation of the Nafion membrane in a highly robust and repeatable fashion, necessary for the practical application of glucose fuel cells in medical implants, poses a particular challenge. We describe fabrication techniques to solve these challenges. Experimental measurements from half-open-geometry glucose fuel cells, which we fabricated on a silicon wafer, are comparable to those obtained from similar electrochemical configurations.

References
[1] R. Sarpeshkar, Ultra Low Power Bioelectronics: Fundamentals, Biomedical Applications, and Bio-inspired Systems, Cambridge University Press, Cambridge, 2010.
[2] W. Wattanapanitch and R. Sarpeshkar, “An Energy-Efficient Micropower Neural Recording Amplifier”, IEEE Transactions on Biomedical Circuits and Systems, Vol. 1, No. 2, pp. 136-147, 2007.
[3] S. K. Arfin and R. Sarpeshkar, “An energy-efficient, adiabatic electrode stimulator with inducitive energy recycling and feedback current regulation”, IEEE Transactions on Biomedical Circuits and Systems, Vol. 6, No. 1, pp. 1-14, 2012.
[4] B. Rapoport and R. Sarpeshkar, “Efficient Universal Computing Architectures for Decoding Neural Activity”, PLoS ONE 7(9): e42492. Doi:10.1371/journal.pone.0042492, 2012.
[5] M. Baker and R. Sarpeshkar, “Feedback analysis and design of RF power links for low-power bionic systems”, IEEE Transactions on Biomedical Circuits and Systems, Vol. 1, No. 1, pp. 28-38, 2007.
[6] S. Mandal and R. Sarpeshkar, “Power-efficient impedance-modulation wireless data links for biomedical implants”, IEEE Transactions on Biomedical Circuits and Systems, Vol. 2, No. 4, pp. 301-315, 2008.
[7] B. Rapoport, J. Kedzierski, and R. Sarpeshkar, “A Glucose Fuel Cell for Implantable Brain-Machine Interfaces”, PLoS ONE, 7(6): e38436, doi:10.1371/journal.pone.0038436, 2012.
[8] C. Salthouse, J. J. Sit, M. Baker, S. Zhak, T. Lu, L. Turicchia, and S. Balster, “An ultra-low-power programmable analog bionic-ear processor”, IEEE Transactions on Biomedical Circuits and Systems, Vol. 52, No. 4, 2005.