Hariprasad Chandrakumar

A Simple Area-Efficient Ripple-Rejection Technique for Chopped Biosignal Amplifiers

Chopping is an effective way to mitigate transistor flicker noise in biosignal amplifiers. However, chopping creates a ripple at the output of the amplifier, which is due to the upmodulated amplifier offset and flicker noise. Many techniques have been suggested in the literature to reduce these ripples. This brief presents a ripple-reduction technique that is far simpler than previous techniques, yet effective and area efficient while consuming no additional power. Simulations using a 65-nm complementary metal-oxide-semiconductor process show that the output ripples are attenuated by 78 dB, whereas all other performance parameters of the amplifier remain unchanged.

28.6 A ±50mV linear-input-range VCO-based neural-recording front-end with digital nonlinearity correction

Neural signal recordings have been an essential tool for understanding the brain and driving the progress in neuroscience research and therapy. The local field potential (LFP) signals, which span from 3Hz to about 200Hz, serve as indicators of various neurological behaviors and disorders. Prior integrated LFP recording front-ends are designed for a small-signal input of a few mV, limiting the dynamic range to <;60 dB [1-3]. For closed-loop neuromodulation, featuring simultaneous neural recording and stimulation, it is important to observe and understand brain dynamics during stimulation. Stimulation artifacts can range between 10 and 100mV and last for several milliseconds. The stimulation patterns result in significant artifact power inside the LFP band that cannot be filtered using conventional techniques. To adaptively reject stimulation artifacts in the digital domain, it is desired to capture the neural signal combined with the artifact with a high linearity. This requirement pushes the front-end dynamic range requirement to about 80dB for ±50mV input range, 20dB beyond the capabilities of current integrated recording front-ends.

5.5 A 2µW 40mVpp linear-input-range chopper- stabilized bio-signal amplifier with boosted input impedance of 300MΩ and electrode-offset filtering

Modern neuromodulation requires closed-loop functionality, where neural recordings are used to adapt stimulation patterns in real time. A closed-loop system requires the neural sensing front-end to record small neural signals in the presence of large stimulation artifacts. The amplitude of artifacts can be a few 10s of mV, and their power is usually in the same frequency band as the signals of interest, requiring non-traditional adaptive filtering to attenuate the artifacts. This requires a sensing front-end that can handle large signals while maintaining the signal integrity of the accompanying small neural signals. State-of-the-art front-ends saturate beyond an input of ~5mV and have limited linearity, making them incapable of handling large artifacts. This work presents a front-end that can tolerate up to ±20mV artifacts in the signal band of 1Hz to 5kHz. To digitize a 1mV neural signal to 8 bits in the presence of a 20mV artifact, the front-end requires a 12b linearity.

A ±50-mV Linear-Input-Range VCO-Based Neural-Recording Front-End With Digital Nonlinearity Correction

Closed-loop neuromodulation is an essential function in future neural implants for delivering efficient and effective therapy. However, a closed-loop system requires the neural-recording front-end to handle large stimulation artifacts-a feature not supported by most state-of-the-art designs. In this paper, we present a neural-recording front-end that has an input range of ±50 mV and can be used in closed-loop systems. The proposed front-end avoids the saturation due to stimulation artifacts by employing a voltage-controlled oscillator (VCO) to directly convert the input signal into the frequency domain. The VCO nonlinearity is corrected using area-efficient foreground polynomial correction. Implemented in a 40-nm CMOS process, the design occupies 0.135 mm2 with an analog power of 3 μW and a digital switching power of 4 μW. It achieves ten times higher linear input range than prior art, and 79-dB spurious-free dynamic range at peak input, with an input-referred noise of 5.2 μVrms across the local-field-potential band of 1-200 Hz. With on-chip subhertz high-pass filters realized by duty-cycled resistors, the front-end also eliminates the need of off-chip dc-blocking capacitors.

Robust, reconfigurable, and power-efficient biosignal recording systems

Wireless sensing of electrophysiological signals in day-to-day life will enable various clinical, research, and wellness applications. This paper reviews the design requirements of biosignal recording interfaces for use in remote, unconstrained environments and put the performance achieved by state-of-the-art designs in perspective. In particular, we emphasize the need for biosignal recording front-ends to provide a dynamic range of approximately 100 dB, while meeting an input-referred noise level of a few μVrms. It is difficult to achieve a low input-referred noise and a high dynamic range using conventional voltage-domain amplifiers; state-of-the-art designs provide only ~60 dB of dynamic range. We propose to process electrophysiological signals in the phase domain, since there is no physical bound on phase. Low-noise VCO-based front-ends can be designed to extend the dynamic range by 40dB without paying a significant power, noise, or area penalty compared to state-of-the-art biosignal recording front-ends. For high-channel-count action-potential recording systems the system power is dominated by the transmitter if raw data is transmitted. In spite of the power reduction achieved by innovative biomedical transmitter designs, on-chip processing becomes necessary to reduce the output data rate for many-channel recording systems. It is important for neuroscientists and electrical engineers to agree upon a scheme to reduce the output data rate. We enlist and discuss a few data-rate reduction options for action-potential recordings. In addition, it is also desirable to make the biosignal sensors self-powered, thus avoiding the need for battery replacement/recharging. We briefly review existing energy-harvesting techniques and discuss future directions.

22.7 An inductively-coupled wireless power-transfer system that is immune to distance and load variations

Biomedical implants are often powered by an external source via an inductively-coupled wireless power link. During actual use the distance between the external and implant coils may change, their axes may misalign, and the load current may change significantly in implants that alternate between monitoring and stimulation. Ideally the wireless power link should deliver a stable voltage to the implant across reasonable ranges of these variations, thereby eliminating a lossy voltage limiter and alignment magnets. We present the analysis and design of a new link architecture to meet all these objectives. It stands apart from similar work in that this link self-regulates against all variations, with no need for a reverse channel.

27.1 A 2.8µW 80mV pp -linear-input-range 1.6GΩ-input impedance bio-signal chopper amplifier tolerant to common-mode interference up to 650mV pp

Closed-loop neuromodulation with simultaneous stimulation and sensing is desired to administer therapy in patients suffering from drug-resistant neurological ailments. However, stimulation generates large artifacts at the recording sites, which saturate traditional front-ends. The common-mode (CM) artifact can be ∼500mV, and the differential-mode (DM) artifact is 50 to 100mV. This work presents a neural recording chopper amplifier that can tolerate 80mV<inf>pp</inf> DM and 650mV<inf>pp</inf> CM artifacts in a signal band of 1Hz to 5kHz. To digitize a 2mV<inf>pp</inf> neural signal to 8b accompanied by an 80mV<inf>pp</inf> DM artifact requires a linearity of 80dB. Neural recording front-ends also need to function within a power budget of 3 to 5µW/ch, input-referred noise of 4 to 8µV<inf>rms</inf>, DC input impedance Z<inf>in</inf>>1GΩ and high-pass cutoff of 1Hz [1,2]. Prior work has addressed power and noise [2–6], but has low Z<inf>in</inf> and limited input signal range, making them incapable of performing true closed-loop operation.