This paper reviews the design and optimization of bioimpedance detection chips, focusing on the applicable scenarios of dual-electrode and quad-electrode and their trade-offs in measurement accuracy and portability. According to different detection requirements, the implementation principles and characteristics of ADC method, DAC method, successive approximation method, half-sine DAC method and baseline elimination technology are discussed in detail. Studies have shown that dual-electrode combined with efficient DAC method has significant advantages in portable devices, while the four-electrode configuration is suitable for high-precision impedance measurement scenarios. This paper provides theoretical support for the design of bioimpedance detection chips and looks forward to its application prospects in wearable medical devices and dynamic monitoring.

Brain organoids are three-dimensional cell aggregates produced in vitro through the self-organization and differentiation of human pluripotent stem cells, which can partially recapiculate the structure and function of the brain in humans. Microelectrode array (MEA) technology is capable of high-throughput detecting the electrophysiological activities of brain organoids with low damage, and high spatiotemporal resolution, thereby providing an efficient platform for the functional characterization of brain organoid neural networks. The integration of brain organoids with MEA technology has attracted widespread attentions in the fields of nervous system development and disease mechanism research, biological neural network intelligent computing, and in vivo repair. In the area of nervous system development and disease mechanism research, MEA technology can long-term track the dynamic developmental process of brain organoids in real time and probe the pathogenesis of diseases by detecting the electrophysiological activities of brain organoids derived from various nervous system diseases. In the avenue of biological neural network intelligent computing research, brain organoids are excellent computing devices attributed to their heterogeneous three-dimensional network structures and plasticity. Therefore, a highly efficient computing platform with low energy cost can be constructed through interaction of brain organoids with MEA. Furthermore, MEA technology shows potential technical application prospects in nervous system repair based on brain organoids.

Electrical stimulation technology has been widely applied in various biomedical fields, including cardiac pacemakers, cochlear implants, muscle reconstruction, vision restoration, and epilepsy suppression. Compared to traditional drug therapies or surgical methods, electrical stimulation offers advantages such as reduced invasiveness, greater flexibility, improved recoverability, and the elimination of risks associated with drug dependency and addiction. Due to the advantages of integrated circuits, including low power consumption, high reliability, strong programmability, ease of multifunctional integration, and suitability for mass production, they have recently become the primary choice for designing electrical stimulators, meeting the demands for miniaturized, intelligent, and cost-effective biomedical applications. However, the integration of high-density electrodes with stimulation-generating circuits presents significant challenges in designing electrode-tissue interfaces. This paper begins with the electrode-tissue interface and provides a comprehensive overview of integrated circuit design for implantable electrical stimulators, including fundamental driver circuit topologies and high-performance complex designs. It emphasizes the analysis of the reliability and safety of biomedical implantable chips and introduces innovative designs that integrate stimulators with energy harvesting modules in closed-loop systems. This review also discusses the future directions of electrical stimulation technology and interface systems including our research group's work on electrical stimulation and interface circuits.

With the advancement of research on single-cell heterogeneity, cellular electrophysical properties have become a crucial focus in disease diagnosis and precision medicine. Microfluidic bioelectrical impedance detection chips measure the impedance changes of cells in an electric field with high precision, enabling the label-free acquisition of cellular electrophysical characteristics such as cell size, membrane capacitance, and cytoplasmic conductivity. This significantly enhances the ability to detect cellular heterogeneity. Compared to traditional methods, microfluidic bioelectrical impedance detection chip technology offers advantages such as high sensitivity, simplicity of operation, and non-destructive detection, demonstrating broad application prospects in early disease diagnosis, drug screening, and personalized treatment. This review first elaborates on the basic principles and system design of the technology, then analyzes the progress in optimizing microfluidic channels and electrode configurations, and discusses its applications in cell classification, drug evaluation, and other fields. Finally, the review examines current technical challenges and future development trends, highlighting its potential for widespread application in precision medicine and early disease diagnosis.

In this paper, an 8-bit current-steering Digital to Analog Converter(DAC) based on Metal Oxide Thin Film Transistor (MO-TFT) technology is designed, which includes a timing refresh module, a synchronous register circuit, a segmented decoding circuit, a switch driver circuit, a switch array and a current source array, a multiplexer network, and a random sequence generator. The timing refresh structure is designed in the digital circuit to solve the traditional bootstrap logic gate charge leakage caused by the current source switch driving voltage drop, to avoid the problem of sampling errors in low frequency signals. A differential dual decoding structure is proposed, so that the open and close signals could reach the switch driver circuit at the same time, ensuring the symmetry of the voltage rise and fall Windows in the driver circuit and reduced the output glitter. Meanwhile, the D flip-flop in the digital circuit and the logic gate in the decoder circuit are used to implement the driver enhancement circuit, guaranteeing the high bit unit current source in the analog circuit can be driven and the conversion rate is improved. Dynamic Elements Matching (DEM) technique is employed to improve the dynamic performance of DAC. The poster-simulation results show that the designed DAC has an area of 73 mm², a power consumption of 6.5 mW, an output current swing of 301.46 μA, a maximum conversion rate of 32 kS/s. Under the condition that the standard deviation of the random matching error of the unit current source is 0.1, the Spurious-Free Dynamic Range (SFDR) at the Nyquist frequency can reach 42.43 dB, the maximum Differential Nonlinearity (DNL) is 0.36 LSB, and the maximum Integral Nonlinearity (INL) is 1.75 LSB, meeting the requirements of flexible electronic systems for biomedical applications.

This paper presents the design and implementation of a surface electromyography (sEMG) signal acquisition and analysis system for assessing the severity of facial palsy and supporting clinical diagnosis. The system consists of three main components: custom-designed sEMG electrodes, an sEMG acquisition device, and a server-based analysis program. The custom electrodes, made of skin-friendly, breathable, and waterproof PU film, improve adhesion and reduce the risk of allergic reactions. These electrodes cover major facial muscle groups, including the frontalis, zygomaticus, orbicularis oculi, and orbicularis oris, enabling precise monitoring of corresponding muscles on both the affected and unaffected sides. The acquisition device employs differential amplification, analog-to-digital conversion, and wireless Bluetooth transmission to collect and transmit sEMG signals to the server in real time. The analysis program processes the signals from three perspectives: motion characteristics, correlation characteristics, and muscle group characteristics, providing quantitative data to assist physicians in determining the location and severity of facial palsy. Experimental results demonstrate that the system effectively differentiates the activity characteristics of affected and unaffected muscles, offering stable performance, ease of operation, and promising application prospects.

Bioimpedance measurement technology serves as a vital tool for medical diagnosis and health monitoring in various emerging medical instruments, in which the high-precision readout circuit is a crucial module for achieving accurate diagnoses. Traditional readout circuits in bioimpedance measurement systems often utilize SAR ADCs, which can hardly achieve an accuracy exceeding 12 bits to meet the increasingly high-precision demands for bioimpedance readout without calibration. To deal with this issue, this paper presents a 24-bit incremental Σ-Δ ADC designed for bioimpedance measurement which adopts a 3^rd-order, 4-bit, single-loop feedforward structure to reduce quantization noise while accelerate the conversion speed. The design incorporates data weighted averaging (DWA) technique to mitigate the nonlinearity caused by multibit feedback DAC’s mismatch, significantly enhancing the ADC’s accuracy. In addition, system-level chopping is employed to eliminate systematic offset in the ADC frontend. A configurable digital filter is designed to achieve flexible tradeoff between measurement time and by configuring different oversampling ratios (OSR). The ADC is fabricated using a 180 nm process, with measurement results under an OSR of 128 showing that the ADC achieves an ENOB of 17.80, a SNR of 108.89 dB, a THD of -113.29 dB, an equivalent input RMS noise of 2.95 μV_RMS. The power consumption is 930 μW for the whole ADC chip.