A lensless blood cell counting system integrating microfluidic channel and a complementary metal oxide semiconductor (CMOS) image sensor is a promising technique to miniaturize the conventional optical lens based imaging system for point-of-care testing (POCT). However, such a system has limited resolution, making it imperative to improve resolution from the system-level using super-resolution (SR) processing. Yet, how to improve resolution towards better cell detection and recognition with low cost of processing resources and without degrading system throughput is still a challenge. In this article, two machine learning based single-frame SR processing types are proposed and compared for lensless blood cell counting, namely the Extreme Learning Machine based SR (ELMSR) and Convolutional Neural Network based SR (CNNSR). Moreover, lensless blood cell counting prototypes using commercial CMOS image sensors and custom designed backside-illuminated CMOS image sensors are demonstrated with ELMSR and CNNSR. When one captured low-resolution lensless cell image is input, an improved high-resolution cell image will be output. The experimental results show that the cell resolution is improved by 4×, and CNNSR has 9.5% improvement over the ELMSR on resolution enhancing performance. The cell counting results also match well with a commercial flow cytometer. Such ELMSR and CNNSR therefore have the potential for efficient resolution improvement in lensless blood cell counting systems towards POCT applications.

Foodborne bacteria, inducing outbreaks of infection or poisoning, have posed great threats to food safety. Potentiometric sensors can identify bacteria levels in food by measuring medium's pH changes. However, most of these sensors face the limitation of low sensitivity and high cost. In this paper, we developed a high-sensitivity ion-sensitive field-effect transistor sensor. It is small sized, cost-efficient, and can be massively fabricated in a standard 65-nm complementary metal–oxide–semiconductor process. A subthreshold pH-to-time-to-voltage conversion scheme was proposed to improve the sensitivity. Furthermore, design parameters, such as chemical sensing area, transistor size, and discharging time, were optimized to enhance the performance. The intrinsic sensitivity of passivation membrane was calculated as 33.2 mV/pH. It was amplified to 123.8 mV/pH with a 0.01-pH resolution, which greatly exceeded 6.3 mV/pH observed in a traditional source-follower based readout structure. The sensing system was applied to Escherichia coli (E. coli) detection with densities ranging from 14 to 140 cfu/mL. Compared to the conventional direct plate counting method (24 h), more efficient sixfold smaller screening time (4 h) was achieved to differentiate samples’ E. coli levels. The demonstrated portable, time-saving, and low-cost prescreen system has great potential for food safety detection.

This paper proposes a neural stimulation integrated circuit design with multiple current output modes. In the cathodic stimulation phase and anodic stimulation phase, each output current waveform can be independently selected to either exponential waveform or square wave, so the stimulator holds four stimulation modes. To minimize the headroom voltage of the output stage and enhance the power efficiency of the proposed stimulator, we introduce the exponentially decaying current which is realized by the exponential current generation circuit in this work. It can enhance the longer duration of the stimulation pulse as well. In case the residual charge may cause harm to patients, a charge balancing technique is implemented in this work for all operation modes. The four-channel stimulator IC is implemented in a 180-nm CMOS process, occupying a core area of 1.93 mm ² . The measurement results show that the proposed stimulator realized a maximum power efficiency of 91.3% and the maximum stimulation duration is 3 times larger than previous works. Moreover, even in exponential output waveform mode, the maximum residual charge in a single cycle is only 255 pC due to the proposed charge balancing technique. The experiment results based on the PBS solution also show that the stimulator IC can remove residual charges within 60 μs, and the electrode voltage remains stable within a safe range under multicycle stimulation.

In recent years, thanks to the development of integrated circuits, clinical medicine has witnessed significant advancements, enabling more efficient and intelligent treatment approaches. Particularly in the field of neuromedical, the utilization of brain-machine interfaces (BMI) has revolutionized the treatment of neurological diseases such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. The BMI acquires neural signals via recording circuits and analyze them to regulate neural stimulator circuits for effective neurological treatment. However, traditional BMI designs, which are often isolated, have given way to closed-loop brain-machine interfaces (CL-BMI) as a contemporary development trend. CL-BMI offers increased integration and accelerated response speed, marking a significant leap forward in neuromedicine. Nonetheless, this advancement comes with its challenges, notably the stimulation artifacts (SA) problem inherent to the structural characteristics of CL-BMI, which poses significant challenges on the neural recording front-ends (NRFE) site. This paper aims to provide a comprehensive overview of technologies addressing artifacts in the NRFE site within CL-BMI. Topics covered will include: (1) understanding and assessing artifacts; (2) exploring the impact of artifacts on traditional neural recording front-ends; (3) reviewing recent technological advancements aimed at addressing artifact-related issues; (4) summarizing and classifying the aforementioned technologies, along with an analysis of future trends.