We present a 6.78-MHz wireless, mode-convertible single-stage resonant charger (SSRC) that provides constant current/constant voltage charging in an extended coupling range for implantable biomedical devices. To extend the charging range in a wireless inductive link for reliable and seamless power transfer, it automatically switches between normal and resonant modes (NM and RM) by sensing current variation induced from the frequency splitting phenomenon, thereby achieving an extended charging distance up to 104.34 %. In addition, the proposed charger limits the maximum current to avoid excessive charging, that potentially degrades the battery's health. The prototype SSRC has been fabricated using a 180 nm bipolar/CMOS/DMOS high voltage process with an active area of 0.575 mm2. The performance of the fabricated chip has been characterized on benchtop and ex vivo using a custom-designed 3-D printed fixture. The measurement results verified efficient power delivery to batteries while extending the charging distance from 23 mm to 47 mm in air and a 20-mm-thick pork slice without over-current issues. The measured peak power conversion efficiencies were 89.42 and 76.6 % in the NM and RM, respectively.

Cardiovascular diseases (CVDs) are among the leading causes of mortality. Traditional diagnostic methods require hospital visits and professional medical personnel, but the timely detection of cardiac conditions can significantly improve survival rates. Therefore, wearable devices with edge-computing capabilities for real-time cardiovascular diagnosis are highly important. Heart sounds provide valuable information on valve closure; however, variations in heart rhythm or heart valve diseases (HVDs) can complicate the identification of affected valves and the interpretation of heart sound origins. Additionally, different disease classifications require distinct model architectures, posing significant challenges for implementation on wearable devices. This study addresses these challenges through three key contributions: an ECG-gating PCG algorithm, improved classification algorithms for arrhythmia and valvular heart disease, and a systolic array-based accelerator with an application-specific instruction-set processor (ASIP) capable of performing inference on multiple models. The algorithms achieve 97.8% and 99.3% accuracy on the MIT-BIH and heart murmur databases, respectively, with hardware quantization errors below 0.5%. The accelerator is fabricated in TSMC 180 nm CMOS technology, achieving an operating power of 414 $μ$W at 1 MHz. The execution times for arrhythmia and valvular heart disease classification are 7.2 ms and 21 ms, respectively, and the energy efficiency normalized to 40 nm is 395.3 GOPS/W. These show that this system can effectively solve the classification of arrhythmia and heart valve diseases.

The therapeutic efficacy of closed-loop neuro-modulation is critically undermined by stimulation artifacts that create a prolonged amplifier "blind period", obscuring neural biomarkers. While state-of-the-art solutions mitigate this by adding complexity around the amplifier-such as active reset, blanking, or digital cancellation-they introduce trade-offs like data loss or computational overhead. In a distinct departure from these approaches, this paper solves the problem at its root by introducing a state-aware feedback element: a self-adaptive pseudo-resistor (A-PR). The A-PR architecture integrates two key innovations: an adaptive Floating Power Supply (FPS) that senses DC errors and autonomously collapses the feedback resistance for rapid recovery, and a process-insensitive Self-Biased Current Source (SBCS) that ensures robust, uniform performance against PVT variations. A complete neural recording front-end featuring the A-PR was fabricated in a 40-nm CMOS process. Measurement results validate the core claims, demonstrating a sub-3-ms recovery time from a 1-V artifact, an input-referred noise of 5.23 $μ$Vrms, and a tunable high-pass corner, all while consuming only 2.3 $μ$W and occupying 0.015 mm². By eliminating the trade-off between fast recovery and high fidelity, the A-PR provides a scalable, low-power solution for next-generation, high-resolution closed-loop neural interfaces.

The increasing data rate has become a major issue confronting next-generation intracortical brain-machine interfaces (iBMIs). The scaling number of recording sites requires complex analog wiring and lead to huge digitization power consumption. Compressive event-based neural frontends have been used in high-density neural implants to support the simultaneous recording of more channels. Event-based frontends (EBF) convert recorded signals into asynchronous digital events via delta modulation and can inherently achieve considerable compression. But EBFs are prone to false events that do not correspond to neural and may affect the output firing rate, which is the key feature for neural decoding. Spike detection (SPD) is a key process in the iBMI pipeline to detect neural spikes and further reduce the data rate. However, conventional digital SPD suffers from the increasing buffer size and frequent memory access power, and conventional spike emphasizers are not compatible with EBFs. In this work we introduced an event-based spike detection (Ev-SPD) algorithm for scalable compressive EBFs. To implement the algorithm effectively, we proposed a novel low-power 10-T eDRAM-SRAM hybrid random-access memory (HRAM) in-memory computing (IMC) bitcell for event processing. We fabricated the proposed 1024-channel IMC SPD macro in a 65nm process and tested the macro with both synthetic dataset and Neuropixel recordings. The proposed macro achieved a high spike detection accuracy of 96.06% on a synthetic dataset and 95.08% similarity and 0.05 firing pattern MAE on Neuropixel recordings. Our event-based IMC SPD macro achieved a high per channel spike detection energy efficiency of 23.9 nW per channel and an area efficiency of 375 μm2 per channel. Our work presented a SPD scheme compatible with compressive EBFs for high-density iBMIs, achieving ultra-low power consumption with an IMC architecture while maintaining considerable accuracy.

We present in this paper an implantable closed-loop neuromodulation prototype system for type 2 diabetes (T2D) management, which leverages pancreatic electrophysiology as both a sensing and therapeutic modality. Among candidate biomarkers, the fraction of plateau phase (FOPP) emerges as a robust indicator of glucose dynamics. Hence, the neural interface is optimized for low-power measurement of the electrical activity of the beta-cells with high accuracy in direct readout mode and long-term monitoring in FOPP mode. The experimental framework was established using a perfused pancreas model, first in mice and then optimized for rats, with glucose-dependent signals captured via a custom 16-channel neural interface. Results confirmed the feasibility of extracting FOPP in ex vivo settings, though signal complexity differed from isolated islets in vitro. Additionally, a fabricated 8-channel electrical stimulator with adjustable current levels and optimized charge balancing technique, demonstrated the capability to meet physiological requirements for beta-cell activation. While integration of AI-based classifiers for advanced FOPP-glucose correlation remains a future step, this study establishes the foundational experimental and technological evidence for a next-generation closed-loop neuromodulator.

A behind-the-ear (BTE) integrated interface for mental healthcare applications is presented, featuring optimized BTE electrode configurations and wide multimodal biomedical IC with adaptive compensation capabilities. The proposed IC supports 8 bio-potential (ExG), 1 photoplethysmogram (PPG), 1 galvanic skin response (GSR), 1 bio-impedance (BioZ), and 2 stimulation channels. The ExG channel achieves 2.5GΩ input impedance, boosted by 308 times with offset compensated auxiliary path (OCAP) architecture, and its AC input impedancecharacteristic is boosted further by dual resolution external positive feedback loop (DR-EPFL) scheme. An area and energy-efficient GSR-embedded ECG recording scheme is presented. For comprehensive multimodal sensing features, dual-slope PPG channel with parasitic capacitance compensation, electrode-tissue impedance adaptive stimulator, and high dynamic range BioZ channel are integrated. The IC was fabricated in a 0.18-μm BCD process and integrated into a BTE patch-type device prototype. System-level feasibility was experimentally verified through in-vivo stress measurements with virtual reality (VR) environment, demonstrating effective mental health monitoring capabilities.

Surgical tumor resection aims to remove all cancer cells in the tumor margin and at centimeter-scale depths below the tissue surface. During surgery, microscopic clusters of disease are intraoperatively difficult to visualize and are often left behind, significantly increasing the risk of cancer recurrence. Radioguided surgery (RGS) has shown the ability to selectively tag cancer cells with gamma (γ) photon emitting radioisotopes to identify them, but require a mm-scale γ photon spectrometer to localize the position of these cells in the tissue margin (i.e., a function of incident γ photon energy) with high specificity. Here we present a 9.9 mm2 integrated circuit (IC)-based γ spectrometer implemented in 180 nm CMOS, to enable the measurement of single γ photons and their incident energy with sub-keV energy resolution. We use small 2 × 2 μm reverse-biased diodes that have low depletion region capacitance, and therefore produce millivolt-scale voltage signals in response to the small charge generated by incident γ photons. A low-power energy spectrometry method is implemented by measuring the decay time it takes for the generated voltage signal to settle back to DC after a γ detection event, instead of measuring the voltage drop directly. This spectrometry method is implemented in three different pixel architectures that allow for configurable pixel sensitivity, energy-resolution, and energy dynamic range based on the widely heterogenous surgical and patient presentation in RGS. The spectrometer was tested with three common γ-emitting radioisotopes (64Cu, 133Ba, 177Lu), and is able to resolve activities down to 1 μCi with sub-keV energy resolution and 1.315 MeV energy dynamic range, using 5-minute acquisitions.

TThis article presents the first co-designed MRI imaging and magnetic positioning system for real-time dynamic motion compensation, achieving sub-millimeter tracking accuracy while preserving diagnostic image quality. The core innovation lies in a system-level co-design of an MRI imaging system and a magnetic localization system, featuring a customized receiver IC for processing magnetic signals coupled by the frontend RF coils, enabling artifact-free MRI imaging in dynamic scenarios. This integration enables a median positioning accuracy of 0.66 mm across a 40×40×50 cm³ field-of-view with a total power consumption of 997 $μ$W. The key innovations include: 1) a time-division multiplexing scheme to enable signal detection from different coils while achieving spectral isolation between 1.4 MHz positioning signals and MRI Larmor frequencies through FPGA-synchronized blanking; 2) a dynamic calibration algorithm fusing magnetic tracking data with multi-frame MRI imaging, reducing spatial blur radius by 40% via weighted averaging; 3) an MRI-optimized Levenberg-Marquardt algorithm incorporating dynamic magnetic beacon weighting and spatial constraints, improving localization accuracy by 53% versus conventional algorithm. The system utilizes planar magnetic beacons with a dimension of 3×3 cm², reducing spatial occupancy compared to prior designs. This work bridges critical gaps between high-precision tracking and artifact-free MRI, enabling real-time imaging of non-autonomous motion and respiratory motion compensation, representing a paradigm shift for MRI-guided interventions.