A Wireless Neural Stimulator IC for Cortical Visual Prosthesis.

We propose a 0.25 × 0.25 × 0.3 mm (~0.02 mm3) optically powered mote for visual cortex stimulation to restore vision. Up to 1024 implanted motes can be individually addressed. The complete StiMote system was confirmed fully functional when optically powered and cortex stimulation was confirmed in-vivo with a live rat brain.

Rapid Real-time Squiggle Classification for Read until using RawMap.

ReadUntil enables Oxford Nanopore Technology's (ONT) sequencers to selectively sequence reads of target species in real-time. This enables efficient microbial enrichment for applications such as microbial abundance estimation and is particularly beneficial for metagenomic samples with a very high fraction of non-target reads (> 99% can be human reads). However, read-until requires a fast and accurate software filter that analyzes a short prefix of a read and determines if it belongs to a microbe of interest (target) or not. The baseline Read Until pipeline uses a deep neural network-based basecaller called Guppy and is slow and inaccurate for this task (~60% of bases sequenced are unclassified). We present RawMap, an efficient CPU-only microbial species-agnostic Read Until classifier for filtering non-target human reads in the squiggle space. RawMap uses a Support Vector Machine (SVM), which is trained to distinguish human from microbe using non-linear and non-stationary characteristics of ONT's squiggle output (continuous electrical signals). Compared to the baseline Read Until pipeline, RawMap is a 1327X faster classifier and significantly improves the sequencing time and cost, and compute time savings. We show that RawMap augmented pipelines reduce sequencing time and cost by ~24% and computing cost by 22%. Additionally, since RawMap is agnostic to microbial species, it can also classify microbial species it is not trained on. We also discuss how RawMap may be used as an alternative to the RT-PCR test for viral load quantification of SARS-CoV-2.

nPoRe: n-polymer realigner for improved pileup-based variant calling.

Despite recent improvements in nanopore basecalling accuracy, germline variant calling of small insertions and deletions (INDELs) remains poor. Although precision and recall for single nucleotide polymorphisms (SNPs) now exceeds 99.5%, INDEL recall remains below 80% for standard R9.4.1 flow cells. We show that read phasing and realignment can recover a significant portion of false negative INDELs. In particular, we extend Needleman-Wunsch affine gap alignment by introducing new gap penalties for more accurately aligning repeated n-polymer sequences such as homopolymers ([Formula: see text]) and tandem repeats ([Formula: see text]). At the same precision, haplotype phasing improves INDEL recall from 63.76 to [Formula: see text] and nPoRe realignment improves it further to [Formula: see text].

A Light-Tolerant Wireless Neural Recording IC for Motor Prediction With Near-Infrared-Based Power and Data Telemetry.

Miniaturized and wireless near-infrared (NIR) based neural recorders with optical powering and data telemetry have been introduced as a promising approach for safe long-term monitoring with the smallest physical dimension among state-of-the-art standalone recorders. However, a main challenge for the NIR based neural recording ICs is to maintain robust operation in the presence of light-induced parasitic short circuit current from junction diodes. This is especially true when the signal currents are kept small to reduce power consumption. In this work, we present a light-tolerant and low-power neural recording IC for motor prediction that can fully function in up to 300 µW/mm2 of light exposure. It achieves best-in-class power consumption of 0.57 µW at 38° C with a 4.1 NEF pseudo-resistorless amplifier, an on-chip neural feature extractor, and individual mote level gain control. Applying the 20-channel pre-recorded neural signals of a monkey, the IC predicts finger position and velocity with correlation coefficient up to 0.870 and 0.569, respectively, with individual mote level gain control enabled. In addition, wireless measurement is demonstrated through optical power and data telemetry using a custom PV/LED GaAs chip wire bonded to the proposed IC.

Ultra-rapid somatic variant detection via real-time targeted amplicon sequencing.

Molecular markers are essential for cancer diagnosis, clinical trial enrollment, and some surgical decision making, motivating ultra-rapid, intraoperative variant detection. Sequencing-based detection is considered the gold standard approach, but typically takes hours to perform due to time-consuming DNA extraction, targeted amplification, and library preparation times. In this work, we present a proof-of-principle approach for sub-1 hour targeted variant detection using real-time DNA sequencers. By modifying existing protocols, optimizing for diagnostic time-to-result, we demonstrate confirmation of a hot-spot mutation from tumor tissue in ~52 minutes. To further reduce time, we explore rapid, targeted Loop-mediated Isothermal Amplification (LAMP) and design a bioinformatics tool-LAMPrey-to process sequenced LAMP product. LAMPrey's concatemer aware alignment algorithm is designed to maximize recovery of diagnostically relevant information leading to a more rapid detection versus standard read alignment approaches. Using LAMPrey, we demonstrate confirmation of a hot-spot mutation (250x support) from tumor tissue in less than 30 minutes.

A low-power communication scheme for wireless, 1000 channel brain-machine interfaces.

Objective. Brain-machine interfaces (BMIs) have the potential to restore motor function but are currently limited by electrode count and long-term recording stability. These challenges may be solved through the use of free-floating 'motes' which wirelessly transmit recorded neural signals, if power consumption can be kept within safe levels when scaling to thousands of motes. Here, we evaluated a pulse-interval modulation (PIM) communication scheme for infrared (IR)-based motes that aims to reduce the wireless data rate and system power consumption.Approach. To test PIM's ability to efficiently communicate neural information, we simulated the communication scheme in a real-time closed-loop BMI with non-human primates. Additionally, we performed circuit simulations of an IR-based 1000-mote system to calculate communication accuracy and total power consumption.Main results. We found that PIM at 1 kb/s per channel maintained strong correlations with true firing rate and matched online BMI performance of a traditional wired system. Closed-loop BMI tests suggest that lags as small as 30 ms can have significant performance effects. Finally, unlike other IR communication schemes, PIM is feasible in terms of power, and neural data can accurately be recovered on a receiver using 3 mW for 1000 channels.Significance.These results suggest that PIM-based communication could significantly reduce power usage of wireless motes to enable higher channel-counts for high-performance BMIs.

A Power-Efficient Brain-Machine Interface System With a Sub-mw Feature Extraction and Decoding ASIC Demonstrated in Nonhuman Primates.

Intracortical brain-machine interfaces have shown promise for restoring function to people with paralysis, but their translation to portable and implantable devices is hindered by their high power consumption. Recent devices have drastically reduced power consumption compared to standard experimental brain-machine interfaces, but still require wired or wireless connections to computing hardware for feature extraction and inference. Here, we introduce a Neural Recording And Decoding (NeuRAD) application specific integrated circuit (ASIC) in 180 nm CMOS that can extract neural spiking features and predict two-dimensional behaviors in real-time. To reduce amplifier and feature extraction power consumption, the NeuRAD has a hardware accelerator for extracting spiking band power (SBP) from intracortical spiking signals and includes an M0 processor with a fixed-point Matrix Acceleration Unit (MAU) for efficient and flexible decoding. We validated device functionality by recording SBP from a nonhuman primate implanted with a Utah microelectrode array and predicting the one- and two-dimensional finger movements the monkey was attempting to execute in closed-loop using a steady-state Kalman filter (SSKF). Using the NeuRAD's real-time predictions, the monkey achieved 100% success rate and 0.82 s mean target acquisition time to control one-dimensional finger movements using just 581 µW. To predict two-dimensional finger movements, the NeuRAD consumed 588 µW to enable the monkey to achieve a 96% success rate and 2.4 s mean acquisition time. By employing SBP, ASIC brain-machine interfaces can close the gap to enable fully implantable therapies for people with paralysis.

SquiggleNet: real-time, direct classification of nanopore signals.

We present SquiggleNet, the first deep-learning model that can classify nanopore reads directly from their electrical signals. SquiggleNet operates faster than DNA passes through the pore, allowing real-time classification and read ejection. Using 1 s of sequencing data, the classifier achieves significantly higher accuracy than base calling followed by sequence alignment. Our approach is also faster and requires an order of magnitude less memory than alignment-based approaches. SquiggleNet distinguished human from bacterial DNA with over 90% accuracy, generalized to unseen bacterial species in a human respiratory meta genome sample, and accurately classified sequences containing human long interspersed repeat elements.

Bridging the"Last Millimeter" Gap of Brain-Machine Interfaces via Near-Infrared Wireless Power Transfer and Data Communications.

Arrays of floating neural sensors with high channel count that cover an area of square centimeters and larger would be transformative for neural engineering and brain-machine interfaces. Meeting the power and wireless data communications requirements within the size constraints for each neural sensor has been elusive due to the need to incorporate sensing, computing, communications, and power functionality in a package of approximately 100 micrometers on a side. In this work, we demonstrate a near infrared optical power and data communication link for a neural recording system that satisfies size requirements to achieve dense arrays and power requirements to prevent tissue heating. The optical link is demonstrated using an integrated optoelectronic device consisting of a tandem photovoltaic cell and microscale light emitting diode. End-to-end functionality of a wireless neural link within system constraints is demonstrated using a pre-recorded neural signal between a self-powered CMOS integrated circuit and single photon avalanche photodiode.

Millimeter-sized smart sensors reveal that a solar refuge protects tree snail Partula hyalina from extirpation.

Pacific Island land snails are highly endangered due in part to misguided biological control programs employing the alien predator Euglandina rosea. Its victims include the fabled Society Island partulid tree snail fauna, but a few members have avoided extirpation in the wild, including the distinctly white-shelled Partula hyalina. High albedo shell coloration can facilitate land snail survival in open, sunlit environments and we hypothesized that P. hyalina has a solar refuge from the predator. We developed a 2.2 × 4.8 × 2.4 mm smart solar sensor to test this hypothesis and found that extant P. hyalina populations on Tahiti are restricted to forest edge habitats, where they are routinely exposed to significantly higher solar radiation levels than those endured by the predator. Long-term survival of this species on Tahiti may require proactive conservation of its forest edge solar refugia and our study demonstrates the utility of miniaturized smart sensors in invertebrate ecology and conservation.

A Light Tolerant Neural Recording IC for Near-Infrared-Powered Free Floating Motes.

A key challenge for near-infrared (NIR) powered neural recording ICs is to maintain robust operation in the presence of parasitic short circuit current from junction diodes when exposed to light. This is especially so when intentional currents are kept small to reduce power consumption. We present a neural recording IC that is tolerant up to 300 µW/mm2 light exposure (above tissue limit) and consumes 0.57 µW at 38°C, making it lowest power among standalone motes while incorporating on-chip feature extraction and individual gain control.

Dual-Junction GaAs Photovoltaics for Low Irradiance Wireless Power Transfer in Submillimeter-Scale Sensor Nodes.

Dual-junction GaAs photovoltaic (PV) cells and modules at sub millimeter scale are demonstrated for efficient wireless power transfer for Internet of Things (IoT) and bio-implantable applications under low-flux illumination. The dual-junction approach meets demanding requirements for these applications by increasing the output voltage per cell with reduced area losses from isolation and interconnects. A single PV cell (150 µm × 150 µm) based on the dual-junction design demonstrates power conversion efficiency above 22% with greater than 1.2 V output voltage under low-flux 850 nm near-infrared LED illumination at 6.62 µW/mm2, which is sufficient for batteryless operation of miniaturized CMOS IC chips. The output voltage of dual-junction PV modules with 4 series-connected cells demonstrates greater than 5 V for direct battery charging while maintaining a module power conversion efficiency of more than 23%.

A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain-machine interfaces.

The large power requirement of current brain-machine interfaces is a major hindrance to their clinical translation. In basic behavioural tasks, the downsampled magnitude of the 300-1,000 Hz band of spiking activity can predict movement similarly to the threshold crossing rate (TCR) at 30 kilo-samples per second. However, the relationship between such a spiking-band power (SBP) and neural activity remains unclear, as does the capability of using the SBP to decode complicated behaviour. By using simulations of recordings of neural activity, here we show that the SBP is dominated by local single-unit spikes with spatial specificity comparable to or better than that of the TCR, and that the SBP correlates better with the firing rates of lower signal-to-noise-ratio units than the TCR. With non-human primates, in an online task involving the one-dimensional decoding of the movement of finger groups and in an offline two-dimensional cursor-control task, the SBP performed equally well or better than the TCR. The SBP may enhance the decoding performance of neural interfaces while enabling substantial cuts in power consumption.

A Microelectronic Sensor Device Powered by a Small Implantable Biofuel Cell.

Biocatalytic buckypaper electrodes modified with pyrroloquinoline quinone (PQQ)-dependent glucose dehydrogenase and bilirubin oxidase for glucose oxidation and oxygen reduction, respectively, were prepared for their use in a biofuel cell. A small (millimeter-scale; 2×3×2 mm3 ) enzyme-based biofuel cell was tested in a model glucose-containing aqueous solution, in human serum, and as an implanted device in a living gray garden slug (Deroceras reticulatum), producing electrical power in the range of 2-10 µW (depending on the glucose source). A microelectronic temperature-sensing device equipped with a rechargeable supercapacitor, internal data memory and wireless data downloading capability was specifically designed for activation by the biofuel cell. The power management circuit in the device allowed the optimized use of the power provided by the biofuel cell dependent on the sensor operation activity. The whole system (power-producing biofuel cell and power-consuming sensor) operated autonomously by extracting electrical energy from the available environmental source, as exemplified by extracting power from the glucose-containing hemolymph (blood substituting biofluid) in the slug to power the complete temperature sensor system and read out data wirelessly. Other sensor systems operating autonomously in remote locations based on the concept illustrated here are envisaged for monitoring different environmental conditions or can be specially designed for homeland security applications, particularly in detecting bioterrorism threats.

High-Efficiency Photovoltaic Modules on a Chip for Millimeter-Scale Energy Harvesting.

Photovoltaic modules at the mm-scale are demonstrated in this work to power wirelessly interconnected mm-scale sensor systems operating under low flux conditions, enabling applications in the Internet of Things and biological sensors. Module efficiency is found to be limited by perimeter recombination for individual cells, and shunt leakage for the series-connected module configuration. We utilize GaAs and AlGaAs junction barrier isolation between interconnected cells to dramatically reduce shunt leakage current. A photovoltaic module with eight series-connected cells and total area of 1.27-mm2 demonstrates a power conversion efficiency of greater than 26 % under low-flux near infrared illumination (850 nm at 1 µW/mm2). The output voltage of the module is greater than 5 V, providing a voltage up-conversion efficiency of more than 90 %. We demonstrate direct photovoltaic charging of a 16 µAh pair of thin-film lithium-ion batteries under dim light conditions, enabling the perpetual operation of practical mm-scale wirelessly interconnected systems.

Infrared Energy Harvesting in Millimeter-Scale GaAs Photovoltaics.

The design and characterization of mm-scale GaAs photovoltaic cells are presented and demonstrate highly efficient energy harvesting in the near infrared. Device performance is improved dramatically by optimization of the device structure for the near-infrared spectral region and improving surface and sidewall passivation with ammonium sulfide treatment and subsequent silicon nitride deposition. The power conversion efficiency of a 6.4 mm2 cell under 660 nW/mm2 NIR illumination at 850 nm is greater than 30 %, which is higher than commercial crystalline silicon solar cells under similar illumination conditions. Critical performance limiting factors of sub-mm scale GaAs photovoltaic cells are addressed and compared to theoretical calculations.

Subcutaneous Photovoltaic Infrared Energy Harvesting for Bio-Implantable Devices.

Wireless biomedical implantable devices on the mm-scale enable a wide range of applications for human health, safety, and identification, though energy harvesting and power generation are still looming challenges that impede their widespread application. Energy scavenging approaches to power biomedical implants have included thermal [1-3], kinetic [4-6], radio-frequency [7-11] and radiative sources [12-14]. However, the achievement of efficient energy scavenging for biomedical implants at the mm-scale has been elusive. Here we show that photovoltaic cells at the mm-scale can achieve a power conversion efficiency of more than 17 % for silicon and 31 % for GaAs under 1.06 µW/mm2 infrared irradiation at 850 nm. Finally, these photovoltaic cells demonstrate highly efficient energy harvesting through biological tissue from ambient sunlight, or irradiation from infrared sources such as used in present-day surveillance systems, by utilizing the near infrared (NIR) transparency window between the 650 nm and 950 nm wavelength range [15-17].

Autonomous Microsystems for Downhole Applications: Design Challenges, Current State, and Initial Test Results.

This paper describes two platforms for autonomous sensing microsystems that are intended for deployment in chemically corrosive environments at elevated temperatures and pressures. Following the deployment period, the microsystems are retrieved, recharged, and interrogated wirelessly at close proximity. The first platform is the Michigan Micro Mote for High Temperature (M³HT), a chip stack 2.9 × 1.1 × 1.5 mm³ in size. It uses RF communications to support pre-deployment and post-retrieval functions, and it uses customized electronics to achieve ultralow power consumption, permitting the use of a chip-scale battery. The second platform is the Environmental Logging Microsystem (ELM). This system, which is 6.5 × 6.3 × 4.5 mm³ in size, uses the smallest suitable off-the-shelf electronic and battery components that are compatible with assembly on a flexible printed circuit board. Data are stored in non-volatile memory, permitting retrieval even after total power loss. Pre-deployment and post-retrieval functions are supported by optical communication. Two types of encapsulation methods are used to withstand high pressure and corrosive environments: an epoxy filled volume is used for the M³HT, and a hollow stainless-steel shell with a sapphire lid is used for both the M³HT and ELM. The encapsulated systems were successfully tested at temperature and pressure reaching 150 °C and 10,000 psi, in environments of concentrated brine, oil, and cement slurry. At elevated temperatures, the limited lifetimes of available batteries constrain the active deployment period to several hours.

Small-area Si Photovoltaics for Low-Flux Infrared Energy Harvesting.

Silicon photovoltaics are prospective candidates to power mm-scale implantable devices. These applications require excellent performance for small-area cells under low-flux illumination condition, which is not commonly achieved for silicon cells due to shunt leakage and recombination losses. Small area (1-10 mm2) silicon photovoltaic cells are studied in this work, where performance improvements are demonstrated using a surface n-type doped emitter and Si3N4 passivation. A power conversion efficiency of more than 17% is achieved for 660 nW/mm2 illumination at 850 nm. The silicon cells demonstrate improved power conversion efficiency and reduced degradation under low illumination conditions in comparison to conventional crystalline silicon photovoltaic cells available commercially.