Designing a low-power wireless communication system involves two major milestones: use of very efficient RF architectures, including circuits for power amplifiers, mixers, etc., as well as employing the appropriate protocol-level algorithms, such as forward error correction (FEC) codes, CRCs, etc. Although these two steps are usually performed in isolation, the result has been extremely successful for designing efficient long-distance communication systems. However, this approach is highly suboptimal for short-range communication systems (i.e., Body Area Networks), where the power consumption of these two components can be comparable ^[1] . For this reason, very careful, system-level analysis is required to achieve the minimum energy consumption in transmitting the required information.

We have designed a flexible, ultra-low-power ISM-band transmitter, including baseband processing and basic protocol functionality (i.e., packetization, CRC calculation), using a 65-nm TSMC process. The simplistic block diagram of the transmitter is shown in Figure 1. The fabricated chip includes four memory banks to store incoming data, a tunable convolutional encoder optimized for short-distance RF modules, and an RF transmitter that utilizes a high-Q FBAR resonator as a local oscillator ^[2] . The transmitter has an output power of ~-10dBm and supports 1Mbps OOK and FSK modulation.  An on-chip FIR filter implements Gaussian pulse shaping for GFSK modulation. In addition to the FEC code, the transmitter has a dedicated accelerator implementing a new form of coding, called network coding (NC) ^[3] , which can increase the reliability of the communication system under challenged channel conditions and potentially reduce the required amount of energy communicating information.

Figure 1: Simplistic block diagram of our ultra-low-power ISM-band transmitter.

1. P. Grover, K. Woyach, and A. Sahai, “Towards a communication-theoretic understanding of system-level power consumption,” IEEE Journal on Selected Areas in Communications, vol. 29, no. 8, pp. 1744-1755, Sept. 2011.
2. A. Paidimarri, “Architecture for ultra-low powermulti-channel transmitters for body area networks using RF Resonators,” Master’s thesis, Massachusetts Institute of Technology, Cambridge, 2011.
3. R. Koetter and M. Medard, “An algebraic approach to network coding,” IEEE/ACM Transactions on Networking, vol. 11, no. 5, pp. 782- 795, Oct. 2003.

Advancements in low-power electronics have opened up many opportunities to provide healthcare solutions through continuous, unobtrusive sensing of vital physiological signs. Power budgets and, by extension, size and cost of such sensors are now dominated by the communication costs, which have not scaled as rapidly ^[1] . We are working on a topology and associated protocols customized for such networks that relax power requirements enough for the network itself to power sensors.

The network consists of clothing made from e-textiles containing a number of strategically-placed inductors screen-printed using a silver paste ^[2] . Sensor Nodes (SNs) can be placed beneath any number of inductors. Power is transferred from the central Base Station (BS) to the SNs in the 27-MHz ISM band, which eliminates the need for bulky energy sources at each SN. Data from the SN is transferred at 1 Mbps through an impedance modulation link, similar to RFID, and is perceived as an ASK waveform by the BS. The high data rate allows each SN to have a very low transmit duty cycle (~1-2 %).

The medium-access protocol is designed to minimize the decision-making burden on the SN. Upon configuring the network, each SN is classified as a “Stream” mode (e.g., EKG, EEG) sensor or a “Contention Access (CA)” mode (e.g., blood pressure, glucose) sensor, which sends data infrequently. The BS assigns fixed timeslots to each stream-mode sensor. Once it has looped through all such sensors, it opens a CA period for the other sensors in the network. The protocol ensures that an SN always waits for a signal from the BS before transmitting and renders synchronization routines, such as the one presented in ^[3] , unnecessary.

1. N. Verma,  A, Shoeb, J. Bohorquez,  J. Dawson, J. Guttag, and A. P. Chandrakasan, “A micro-power EEG acquisition SoC with integrated feature extraction processor for a chronic seizure detection system,” IEEE Journal of Solid-State Circuits, vol. 45, no. 4, pp. 804-816, Apr. 2010.
2. K. Yongsang, K. Hyejung, and Y. Hoi-Jun, “Electrical characterization of screen-printed circuits on the fabric,” IEEE Transactions on Advanced Packaging, vol. 33, no. 1, pp. 196-205, Feb. 2010.
3. O. Omeni, A. Wong, A. J. Burdett, and C. Toumazou, “Energy efficient medium access protocol for wireless medical Body Area SSensor Networks,” IEEE Transactions on Biomedical Circuits and Systems, vol.2, no. 4, pp. 251-259, Dec. 2008.

Continuous tracking of neurological disorders is crucial for the proper diagnosis and medication of epilepsy, and it mandates the design of ultra-low power sensor with a small form factor and continuous EEG classification. The main challenges arise from three factors: 1) variation in seizure pattern from person to person and age to age; 2) the need for wide dynamic range, low-noise AFE with high CMRR; and 3) the area overhead of integrating classification processor to enable seizure monitoring, detection, and storage in one chip. We present an ultra-low-power scalable EEG acquisition SoC for continuous seizure detection and recording with fully integrated patient-specific Support Vector Machine (SVM)-based classification processor. The proposed SoC is composed of 8 high-dynamic range Analog Front-End (AFE) channels, an SRAM and a patient-specific machine-learning seizure classification processor with a Feature Extraction (FE) Engine and a Classification Engine (CE).  Each channel in the AFE integrates a Chopper-Stabilized Capacitive Coupled Instrumentation Amplifier (CS-CCIA) followed by an Analog Signal Processing Unit (ASPU). The SoC maintains high-accuracy seizure detection while minimizing the area overhead of the FE Engine by operating in two separate modes for seizure detection and recording. In seizure detection mode, the AFE uses a bandwidth of 30Hz with a 4-step adapted channel gain according to the signal strength. Once seizure is classified, the SoC automatically runs in seizure-recording mode at 100Hz bandwidth to store the EEG data in the internal SRAM.  Digital filters are implemented using Distributed Quad-LUT (DQ-LUT) architecture, which enables area reduction for full integration of the classification processor. The SoC shows a detection accuracy of 84.4% in a rapid eye blink test while consuming 2.03μJ/classification.

1. J. Yoo, L. Yan, D. El-Damak, M. A. Altaf, A. Shoeb, H.-J. Yoo, and A. P. Chandrakasan, “An 8-channel scalable EEG acquisition SoC with fully integrated patient-specific seizure classification and recording processor,” IEEE Intl. Solid-State Circuits Conference Dig.Tech. Papers, Feb. 2012, pp. 292–293.

An ultrasound image is formed from a collection of ultrasonic beams transmitted and received by an array of transducer elements.  As the resolution of an image and the range over which an image is to be formed increase, so do the number of these transducer elements and the corresponding digital processing units.  The intensive signal processing power required for ultrasound imaging ^[1] means that conventional ultrasound systems are often large and expensive, and this demand for processing power can only worsen as more transducers and signal channels are implemented.  In applications such as point-of-care diagnostics in rural areas, the movement to a portable and low-power ultrasound imaging system is warranted.

Beamforming, which in its simplest form involves delaying, scaling, and summing to produce a coherent signal from the collection of received beams, has been identified as an area for algorithmic research and development ^[2] .  In this work, an 8-channel wide, scalable digital beamformer is implemented with feedback for power reduction.  Two modes of operation are available: coarse and fine beamforming.  In the coarse beamforming mode, digitized data from an evenly spaced subset of transducer elements are processed, providing a low-quality image of the full region of interest, which yields power savings by turning off the analog front end electronics and analog-to-digital converters corresponding to the unused 50% or 75% of array channels (schematically shown in Figure 1).  Figures 2a and b show the coarse images for quarter and half resolution coarse beamforming modes.  Next, the user can specify a smaller region in which a higher quality image is desired, which is then beamformed by the same 8-channel wide processing unit using all available channels (an example of the full region full resolution image is shown in Figure 2c).

1. M. Ali, D. Magee, and U. Dasgupta, “Signal processing overview of ultrasound systems for medical imaging,” Texas Instruments, Dallas, TX, SPRAB12, 2008.
2. S. Stergiopoulos, Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging Real-Time Systems.  Boca Raton: CRC Press, Inc., 2000.

Circuits for wearable vital sign monitors have very stringent requirements on power dissipation due to limited energy storage capacity and the need for a long lifetime.  Extending the time between battery recharge or replacement requires low-power electronics.  We report a micro-watt mixed-signal front-end (MSFE) for ECG monitoring ^[1] that uses aggressive voltage scaling to maximize power-efficiency and ensure compatibility with low-voltage DSPs ^[2] .  The MSFE shown in Figure 1 rejects 50/60Hz power-line interference (PLI) at the input of the system by using a mixed-signal feedback loop, enabling low-voltage operation by reducing dynamic range requirements.  Analog circuits are optimized for ultra-low-voltage, and a SAR ADC with a dual-DAC architecture eliminates the need for a power-hungry ADC buffer. Oversampling and ΔΣ-modulation leveraging integrated digital processing are used to achieve ultra-low-power operation without sacrificing noise performance and dynamic range.  Figure 2 shows ECG measurements on a male subject with the MSFE using gel electrodes and unshielded wiring.  The PLI is clearly canceled when the PLI filter is enabled. The MSFE was prototyped in a 0.18µm CMOS process and consumes 2.9µW from 0.6V.

1. M. Yip, J. L. Bohorquez, and A. P. Chandrakasan, “A 0.6V 2.9µW mixed-signal front-end for ECG monitoring,” IEEE Symposium on VLSI Circuits, pp. 66-67, Honolulu, HI, Jun. 2012.
2. J. Kwong and A. P. Chandrakasan, “An energy-efficient biomedical signal processing platform,” IEEE J. Solid-State Circuits, vol. 46, no. 7, pp. 1742-1753, Jul. 2011.

Understanding certain medical conditions requires understanding specific aspects of the arterial blood flow. For instance, diagnosing atherosclerosis requires capturing detailed flow inside an arterial segment. Such study requires developing accurate solvers for the detailed equations describing both the blood flow and the elastic behavior of the arteries. At the other end of the spectrum, studying hypertension requires computing pressure and averaged flow over a larger arterial network. Such analysis requires developing compact computationally inexpensive models of complex segments of the arterial network. These models relate the pressure and average flow at the terminals of the arterial segments and must be easily interconnected to form complex and large arterial networks.

In this project we are developing a 2-D fluid-structure interaction solver to accurately simulate blood flow in arteries with bends and bifurcations. Such blood flow is mathematically modeled using the incompressible Navier-Stokes equations. The arterial wall is modeled using a linear elasticity model ^[1] . Our solver is based on an enhanced immersed boundary method (IBM) ^[2] . As a second step we are developing system identification techniques ^[3] to generate passive models for complex arterial segments such as large arteries, arterial bends, and bifurcations. We have validated our solver results versus reference results obtained from MERCK Research Laboratories for a straight vessel of length 10 cm and diameter 2 cm. Our results for pressure, flow, and radius variations are within 3% of those obtained from MERCK. Furthermore, we are validating our model results by cascading different models and comparing the results of the resulting network to those predicted by our solver. Our preliminary results for pressure and flow at the terminals of the models are within 10% of those obtained from the full simulator. In addition, with our models we reduce the computational time by more than 100,000 times.

1. A. Quarteroni, M. Tuveri, and A. Veneziani “Computational vascular fluid dynamics: problems, models and methods,” Computing and Visualization in Science, vol. 2, no. 4, pp. 163-97, 2000.
2. C. Peskin and D. McQueen “A three-dimensional computational method for blood flow in the heart I. Immersed elastic fibers in a viscous incompressible fluid.” Journal of Computational Physics, vol. 81, issue 2, pp. 372-405, 1989.
3. B. Bond, T. Moselhy, and L. Daniel, “System identification techniques for modeling of the human arterial system,” in Proc. SIAM Conference on the Life Sciences, Pittsburgh, PA, July 2010, p. 12-15. (invited)

Two recent advances in Magnetic Resonance Imaging (MRI) technology have resulted in a need for sophisticated computational electromagnetics (CEM) tools. The first is the availability of higher fields scans that can improve signal-to-noise ratio. The second is the availability of transmit-coil arrays, which can be used to minimize human-body heating by electric fields. Higher fields imply higher-frequency RF pulses, with wavelengths comparable to the human body dimensions, which complicates electromagnetic analysis. They also imply increased tissue heating, which limits the RF power used for imaging purposes. In the computational prototyping group, we are developing CEM techniques to address these new needs of the MRI community, working in close collaboration with the RLE MRI group and the Harvard Massachusetts General Hospital MRI group, with some specific targets.

First, we are developing fast methods for tuning and matching the transmitters to the human-body loaded MRI coils. We combined scattering-matrix formalism, a frequency-domain finite-elements method, and commercial RF optimization software to reduce this process from days to hours. Also we plan to apply integral-equation methods to reduce it to minutes. Second, we are developing integral methods to allow for efficiently optimizing the geometrical configuration of the transmit coils. This hybrid approach will combine pre-computed Green’s functions for a realistic human body model with method of moments to be able to rapidly assess different coil configurations for a typical body. To aid this assessment, we plan to leverage our work on parameterized model-order reduction, automatically generating models depending on relevant parametric quantities. We are also working on fast methods for computing the approximate solutions to the electromagnetic fields inside the human body, assuming a simplified 3-tissue model that can be obtained for each patient by a quick MRI scan. Finally, we are developing an automated procedure for designing robust decoupling networks for arbitrary MRI transmission coil arrays, based on automatic nonlinear least squares techniques to compute the input impedance matrix, in opposition to currently applied manual methods, limited to small number of channels. These decoupling networks reduce the input power required for the same local increase of body heat vs. excitation fidelity.

Optoelectronic devices intimately integrated on silicon integrated circuits have long been sought for optical intercon-nect applications, optical communications modules, and–more recently–neural stimulation and sensing.  Toward this end we have recently demonstrated a new heterogeneous integration technique for integrating vertical cavity surface emitting lasers (VCSELs) and edge-emitting laser diodes (EELs) on silicon CMOS integrated circuits ^{[1] [2]} .

Fully processed and tested oxide-aperture VCSELs emitting at 850 nm have been fabricated as individual “pills” 55 µm in diameter and 8 µm tall with a disk contact on the n-type backside and a ring contact on the p-type, emitting top-side.  Similarly, 1.55-µm emitting micro-cleaved cavity EEL platelets 5 µm thick, 150 µm wide, and 300 µm long have also been fabricated.  Using a custom micro-pipette vacuum pick-up tool, these micro-laser pills and platelets have been placed on contact pads at the bottom of recesses etched though the dielectric over coating on a Si chip, and batch solder-bonded in place using a custom pressurized-diaphragm bonding apparatus.  Back-end processing of the chip then continues with surface planarization, contact via formation, and interconnect metal deposition and patterning.  An example of a completely integrated VCSEL pill appears in Figure 1.

No adverse effects are seen from fabricating laser diodes as freestanding pills and platelets.  Devices integrated in this manner show the same high performance as devices left on their native substrates and in fact have superior thermal characteristics, largely due to the better thermal conductivity of Si over that of GaAs and InP.

The technique demonstrated in this work offers numerous other advantages over alternative heterogeneous integration techniques.  Both the devices to be integrated, and the target circuit wafers, are fabricated under optimal conditions and are pre-tested and screened prior to integration to insure high yield.   Significantly, many different types of devices can be integrated on the same IC wafer, a feature unique to this approach.  Furthermore, the integration process effectively avoids thermal expansion mismatch limitations and wafer diameter mismatch issues, and it is compatible with parallel assembly techniques, such as fluidic self-assembly.

1. J. M. Perkins, and C. G. Fonstad, “ Full recess integration of small diameter low threshold VCSELs within Si-CMOS ICs,” Optics Express, vol. 16, no. 18 pp. 13955-13960, 2008.
2. J. J. Rumpler and C. G. Fonstad, Jr., “Continuous-wave electrically pumped 1.55 µm edge-emitting platelet ridge laser diodes on silicon,” IEEE Photonics Technology Letters, vol. 21, pp. 827-829, 2009.

Professor Ed Boyden uses light to precisely control neural activity.  His lab has invented safe, effective ways to deliver light-gated membrane proteins to neurons and other excitable cells (e.g., muscle, immune cells, pancreatic cells, etc.) in an enduring fashion, thus making the cells permanently sensitive to being activated or silenced by millisecond-timescale pulses of blue and yellow light, respectively ^[1] .  This ability to modulate neural activity with a temporal precision that approaches that of the neural code itself holds great promise for human health, and his lab has developed animal models of epilepsy and Parkinson’s disease to explore the use of optical control to develop new therapies.

We have recently developed mass-fabricatable multiple light guide microstructures produced using standard microfabrication techniques to deliver light to activate and silence neural target regions along their length as desired ^[2] .  Each probe is a 100- to 150-micron-wide insertable micro-structure with many miniature lightguides running in parallel and delivering light to many points along the axis of insertion.  Such a design maximizes the flexibility and power of optical neural control while minimizing tissue damage.  We are currently developing 2-D arrays of such probes so multiple colors of light can be delivered to 3-dimensional patterns in the brain, at the resolution of tens to hundreds of microns, thus furthering the causal analysis of complex neural circuits and dynamics.  Such devices will allow the substrates that causally contribute to neurological and psychiatric disorders to be systematically analyzed via causal neural control tools.  Given recent efforts to test such reagents in nonhuman primates, these devices may also enable a new generation of optical neural control prosthetics, contributing directly to the alleviation of intractable brain disorders.

The initial light-guide structures have been fabricated from silicon oxynitride clad with silicon dioxide, and tests show excellent transmission of light with no visible loss in the taper and bend regions of the patterns ^[2] .  Significantly, the novel 90˚ bend invented to direct light laterally out the side of the narrow probe functions as designed ^[2] .  The optical sources for initial tests with the probe are independent laser modules coupled to one end of a fiber-optic ribbon cable (see Figure 2).  The other end of the ribbon cable is butt-coupled to the inputs of the probe via a standard fiber-optic connector ferrule.  This allows for increased modularity and control in initial probe testing.

We are now utilizing transgenic mice, which express optogenetic activators and silencers in cortical pyramidal neurons, to demonstrate optogenetic control of neural circuits in a fashion appropriate for in vivo circuit mapping or brain machine interface prototyping.  Our goal is to explore the degree to which this technology can be used to functionally map neural network connectivity over large, multi-region circuits in the brain, and to subserve a new generation of neural control prosthetics.

1. X. Han and E. S. Boyden, “Multiple-color optical activation, silencing, and desynchronization of neural activity, with single-spike temporal resolution,” PLoS ONE, vol. 2, no. 3, p. e299, Mar. 2007.
2. A. N. Zorzos, E. S. Boyden, and C. G. Fonstad, “A multi-waveguide Implantable probe for light delivery to distributed brain targets,” Applied Optics Letters vol. 35, no. 12, pp. 4133-4135, Dec. 2010.

To achieve comfortable form factors for wireless medical devices, battery size, and thus power consumption, must be curtailed.  Often the largest power consumption for wireless medical devices is in storing or transmitting acquired data.  Body area networks (BAN) can alleviate power budgets by using low power transmitters to send data “locally” around the body to receivers that are around areas of the body that allow for larger form factors, like the wrist or the waist.  These receivers, which have larger power budgets, can then process and store the data or send it elsewhere using higher power transmitters.

Body-coupled communication (BCC) shows great potential in forming a BAN.  Traditional two-node BCC works by forming two capacitive links between a transmitter and a receiver, creating a circuit loop.  One of these links is created by both the transmitter and receiver capacitively coupling to the body, effectively using the body as a low-resistance channel between the respective capacitors.  The second link is created by coupling both the transmitter and receiver coupling to the environment, or “earth ground,” and using it as a return path.  Larger BANs can be made by coupling additional nodes to both the body and the environment.

An application for a BAN is for an implanted EEG recorder at the head, to communicate with a base station at the waist.  For implants, the traditional BCC will not work because capacitors E and F will short out capacitors A and D, reducing the transmission line between the transmitter and receiver to that shown in Figure 2. FSK data was sent across this channel in a variety of environments and activities including talking outdoors on a cell phone, exercising at a gym, and doing household chores.  There was no significant difference in the BER across these different environments.

1. T. G. Zimmerman, “Personal area networks (PAN): Near-field intrabody communication,” Master’s thesis, Massachusetts Institute of Technology, Cambridge, 1995.
2. S.-J. Song, N. Cho, S. Kim et al., “A 0.9V 2.6mW body-coupled scalable PHY transceiver for body sensor applications,” ISSCC Dig. Tech. Papers, pp. 366-367, Feb. 2007.
3. A. Fazzi et al., “A 2.75mW wideband correlation-based transceiver for body-coupled communication,” ISSCC Dig. Tech. Papers, pp. 204-205, Feb. 2009.