IEEE transactions on biomedical circuits and systems最新文献

This paper presents an application-specific integrated circuit (ASIC) fabricated using the CMOS 180 nm process to perform simultaneous neural recording and optogenetic stimulation. To perform effective optogenetic stimulation, the ASIC features an advanced switched-capacitor-based stimulation (SCS) driver, called voltage-boosting SCS (VB-SCS). The VB-SCS can drive LED with large current pulses up to 8 mA while reducing the required supply voltage by half, facilitating wireless power reception. To prevent saturation from stimulation-induced artifacts, the ASIC integrates a direct digitizing recording frontend with a high-resolution delta-sigma (ΔΣ) analog-to-digital converter (ADC) that directly digitizes neural signals with a large input dynamic range. This ΔΣ ADC involves a Gm-C integrator followed by a noise-shaping (NS) successive approximation register (SAR) quantizer. Measurement results indicate that this ΔΣ ADC-based direct digitizing frontend can tolerate large artifacts up to 300 mV_PP while linearly digitizing neural signals with an effective number of bits (ENOB) of 11.4 bits, consuming 10.8 μW. The ASIC, together with its associated passive components, was assembled into a headstage for in vivo verification, successfully demonstrating the functionality of the ASIC.

High-throughput biosensor screening and optimization are critical for health and environmental monitoring applications to ensure rapid and accurate detection of biological and chemical targets. Traditional biosensor design and optimization methods involve labor-intensive processes, such as manual pipetting of large sample volumes, making them low throughput and inefficient for large-scale library screenings under various environmental and chemical conditions. We address these challenges by introducing a modular droplet microfluidic system embedded with custom CMOS integrated circuits (ICs) for impedance spectroscopy and bioluminescence detection. Fabricated in a 65 nm process, our CMOS ICs enable efficient droplet detection and analysis. We demonstrate successful sensing of luciferase enzyme-substrate reactions in nL-volume droplets. The impedance spectroscopy chip detects 4 nL droplets at 67 mm/s with a 45 pA resolution, while the luminescence detector senses optical signals from 38 nL droplets with a 6.7 nA/count resolution. We show real-time concurrent use of both detection methods within our hybrid platform for cross-validation. This system greatly advances conventional biosensor testing by increasing flexibility, scalability, and cost-efficiency.

Ultrasound (US) as a wireless power transfer methodology has drawn considerable attention from the implantable medical devices (IMD) research community. Beamforming (BF) using an external transducer array patch (ETAP) has been proposed as a robust localization scheme to find a mm-sized IMD inside the human body. However, for applications focusing on deep and shallow IMDs, optimum resource utilization at the ETAP is a major power efficiency concern for energy-constrained wearable patches. Moreover, misalignment tolerance due to IMD movements (respiratory and patient ambulatory reasons) relative to the ETAP remains a challenge. This paper presents an energy-efficient method to localize a mm-sized IMD through the dynamic selection of a sub-array within the ETAP. It is fully adaptive to the heterogeneity of the media and requires no a priori knowledge of the IMD. To improve the tolerance to IMD movements, tracking is implemented by adding and subtracting elements on the sub-array such that the sub-array electrically follows the IMD movement. Furthermore, it is shown that a minimum sampling frequency of 10X the US frequency can improve the tolerance to random noise. K-wave simulations in MATLAB are performed in different heterogenous, scattering biological media to prove the efficacy of the proposed method over standard BF methods. Measurement results in heterogenous scattering media consisting of a 3D-printed human ribs phantom and a partially blocking multipath cancellous bone phantom show an energy efficiency improvement of 10.53X and 14.4X compared to the delay-and-sum beamforming method and the unfocused transmission employing all the elements of the ETAP, respectively.

This paper presents a reconfigurable bidirectional wireless power and data transceiver (RB-WPDT) integrated circuit (IC) for wearable biomedical applications. The proposed transceiver can be reconfigured as a differential class-D power amplifier or a full-wave rectifier depending on the mode signal to facilitate power transfer between devices. Additionally, the RBWPDT system supports full-duplex (FD) data transmission via a single inductive link, enabling real-time control and monitoring between devices. The proposed FD method utilizes frequency shift-keying pulse-width modulation (FSK-PWM) for downlink and load shift-keying (LSK) for uplink, achieving simultaneous bidirectional data transmission by ensuring that the FSK-PWM downlink and LSK uplink data channels operate independently with minimal interference. The measured downlink and uplink data rates are 250 kb/s and 67 kb/s, respectively. The measured overall DC-to-DC efficiency is 49%, while the power delivered to the load (PDL) is 120 mW at a 5 mm distance. The proposed chip is fabricated using a 180-nm BCD CMOS process.

This article presents a fully integrated CMOS ferrofluidic platform featuring on-chip three-electrode electrochemical cells, temperature regulators, and magnetic sensors. The proposed platform consists of 25 ferrofluidic pixels and 2 magnetic sensors. Each ferrofluidic pixel comprises a spiral inductor, a three-electrode electrochemical cell, a temperature sensor, and a localized Joule heater. Unlike pneumatic-based platforms, this ferrofluidic platform does not require an external pneumatic pump to drive droplets. Instead, the on-chip spiral inductors generate magnetic fields to manipulate the ferrofluidic droplets. Additionally, these inductors are repurposed as heat radiators. The CMOS ferrofluidic platform is implemented using a 45-nm CMOS SOI process. Theoretical analyses of ferrofluidic control and magnetic sensing are conducted to understand the relationship between ferrofluidic movement conditions and the integrated magnetic sensor. The on-chip electrochemical potentiostat is characterized using various concentrations of methylene blue solution, and the variation in the electrochemical sensor is measured. As proof of concept, biological measurements with on-chip real-time recombinase polymerase amplification (RT-RPA) are demonstrated. The proposed platform offers a fully integrated solution for ferrofluidic manipulation, sensing, and temperature regulation without the need for external bulky equipment, thereby supporting advanced biomolecular processing. While RT-RPA is used here solely for demonstration purposes, our ferrofluidic multi-functional CMOS array platform is also capable of processing a wide range of other molecular analytes. This versatility underscores the platform's potential for broad applications in molecular diagnostics and bioanalytical research.

This paper presents a 9-mW ultrasonic through-transmission transceiver (TRX) for portable, non-invasive intracranial pressure (ICP) sensing. It employs two ultrasound transducers placed at the temporal bone windows to measure changes in the ultrasonic time-of-flight (ToF), based on which the skull expansion and the corresponding ICP waveform are derived. Key components include a high-efficiency Class-DE power amplifier (PA) with 95% efficiency and an output swing of 15.8 VPP, along with a successive approximation register (SAR) delay-locked loop (DLL)-based time-to-digital converter (TDC) with 29.8 ps resolution and 122 ns range. Other than electrical characterization, the sensor is validated through two demonstrations using a water tank setup and a human head phantom setup, respectively. It demonstrates a high correlation of R² = 0.93 with a medical-grade invasive ICP sensor. The proposed system offers high accuracy, low power consumption, and reliable performance, making it a promising solution for real-time, portable, non-invasive ICP monitoring in various clinical settings.

Personalized brain implants have the potential to revolutionize the treatment of neurological disorders and augment cognition. Medical implants that deliver therapeutic stimulation in response to detected seizures have already been deployed for the treatment of epilepsy. These devices require low-power integrated circuits for life-long operation. This constraint impedes the integration of machine-learning driven classifiers that could improve treatment outcomes. This paper introduces BrainForest, a neuromorphic multiplier-less bit-serial weight-memory-optimized brain-state classification processor. The architecture achieves state-of-the-art energy efficiency using two layers of neuron models to implement the spectral and temporal functions needed for classification: 1) resonate-and-fire neurons are used to extract physiological signal band energy EEG biomarkers 2) leaky integrator neurons are used to build multi-timescale representations for classification. Sparse neural model firing activity is used to clock-gate device logic, thereby decreasing power consumption by 93%. An energy-optimized 1024-tree boosted decision forest performs the classification used to trigger stimulation in response to detected pathological brain states. The IC is implemented in 65nm CMOS with state-of-the-art power consumption (best case: 9.6μW, typical: 118μW), achieving a seizure sensitivity of 97.5% with a false detection rate of 2.08 per hour.

Cognitive navigation, a high-level and crucial function for organisms' survival in nature, enables autonomous exploration and navigation within the environment. However, most existing works for bio-inspired navigation are implemented with non-neuromorphic computing. This work proposes a bio-inspired memristive spiking neural network (SNN) circuit for goal-oriented navigation, capable of online decision-making through reward-based learning. The circuit comprises three primary modules. The place cell module encodes the agent's spatial position in real-time through Poisson spiking; the action cell module determines the direction of subsequent movement; and the reward-based learning module provides a bio-inspired learning method adaptive to delayed and sparse rewards. To facilitate practical application, the entire SNN is quantized and deployed on a real memristive hardware platform, achieving about a 21× reduction in energy consumption compared to a typical digital acceleration system in the forward computing phase. This work offers an implementation idea of neuromorphic solution for robotic navigation application in low-power scenarios.

Recent advancements in head-mounted wearable technology are revolutionizing the field of biopotential measurement, but the integration of these technologies into practical, user-friendly devices remains challenging due to issues with design intrusiveness, comfort, reliability, and data privacy. To address these challenges, this paper presents GAPSES, a novel smart glasses platform designed for unobtrusive, comfortable, and secure acquisition and processing of electroencephalography (EEG) and electrooculography (EOG) signals.We introduce a direct electrode-electronics interface within a sleek frame design, with custom fully dry soft electrodes to enhance comfort for long wear. The fully assembled glasses, including electronics, weigh 40 g and have a compact size of 160 mm × 145 mm. An integrated parallel ultra-low-power RISC-V processor (GAP9, Greenwaves Technologies) processes data at the edge, thereby eliminating the need for continuous data streaming through a wireless link, enhancing privacy, and increasing system reliability in adverse channel conditions. We demonstrate the broad applicability of the designed prototype through validation in a number of EEG-based interaction tasks, including alpha waves, steady-state visual evoked potential analysis, and motor movement classification. Furthermore, we demonstrate an EEG-based biometric subject recognition task, where we reach a sensitivity and specificity of 98.87% and 99.86% respectively, with only 8 EEG channels and an energy consumption per inference on the edge as low as 121 μJ. Moreover, in an EOG-based eye movement classification task, we reach an accuracy of 96.68% on 11 classes, resulting in an information transfer rate of 94.78 bit/min, which can be further increased to 161.43 bit/min by reducing the accuracy to 81.43%. The deployed implementation has an energy consumption of 40 μJ per inference and a total system power of only 12.4 mW, of which only 1.61% is used for classification, allowing for continuous operation of more than 22 h with a small 75 mAh battery.

In this article, we present a novel approach for designing a low-power, low-area impulse radio ultra-wideband (IR-UWB) noncoherent receiver capable of achieving a data rate of 100 Mbps. Our proposed receiver demonstrates the ability to demodulate ON-OFF keying pulse streams across the entire lower frequency band defined by the Federal Communication Commission for UWB applications. The key components of the proposed receiver include a reconfigurable differential two-stage low-noise amplifier, a fully differential squarer, narrow-band interface rejection filters, and variable gain baseband amplifiers. These circuits work cohesively to ensure efficient signal reception and processing. To validate the performance of the proposed receiver, we implemented the design using TSMC 40-nm CMOS process technology. A short-range communication including a 1.5 cm tissue layer is tested utilizing a typical upconversion UWB transmitter fabricated in the same technology. Remarkably, the proposed receiver achieves a data rate of 100 Mbps with an impressively low energy efficiency of 78.8 pJ/b and occupies an area of 0.705 mm². The compact size, remarkable energy efficiency, and high data rate capabilities of the proposed receiver meet the stringent requirements of neural recording implants.