A high dynamic range and low-noise CMOS (complementary metal-oxide-semiconductor transistor) front-end interface integrated circuit (IC) with multi-channel detection is presented in this paper. Two different current detection channels, composed of a trans-impedance amplifier (TIA) and an integrator-differentiator TIA, are used to boost the current detection range. A capacitance-coupled instrument amplifier (CCIA) is also included to realize high precision voltage detection. A fourth-order sigma-delta modulator using a second-order loop filter and a second-order noise shaping integral quantizer is adopted to realize effective number of bits above 16 bits. The presented interface IC is implemented in 0.18-μm CMOS process with supply voltage of 3.3 V, and a proto-type electrochemical sensor platform with miniaturized sensor array is developed to verify the functionality of the interface IC. Measurement results indicate that the designed interface IC achieves 157.8 dB current detection dynamic range, and the measured input-referred current noise and voltage noise floor are 1.04 pA and 58.4 nV within 10 kHz integration bandwidths, respectively.

The output capacitorless low-dropout regulator (OCL-LDO) has developed rapidly in recent years. This paper presents a flipped voltage follower (FVF) OCL-LDO with fast transient response. By adding a dynamic bias circuit to the FVF circuit, the proposed LDO has the ability to quickly adjust the gate voltage of the power transistor, without extra power consumption. The proposed LDO was designed in 0.18 μm CMOS process. The simulation results show that the recovery time is 52 ns when the load changes from 0.1 mA to 20 mA with a slew rate of 20 mA/ps, while the quiescent current is 92 μA with 1 V regulated output. The undershoot and overshoot voltage are 242 mV and 250 mV, respectively.

White balance is a critical component in portable consumer electronics equipped with cameras, such as digital cameras and smartphones. To further enhance the quality and efficiency of image processing, this study introduces a convolutional neural network-based classifier into the white balance process, enabling white balance functionality. This paper proposes an automatic white balance classification network, automatic white balance classification network applying combined dilated convolution (AWBDILNet), that builds on existing color-distorted image classification models. AWBDILNet replaces standard convolutional layers with combined dilated convolution, marking a novel application in white balance correction. It integrates an activation function that combines ReLU and H-swish, and features a simplified network structure. Additionally, the number of neurons per layer is optimized for binary classification of color-distorted images. An intelligent white balance processing system is developed using the AWBDILNet classifier. In this system, normally balanced images remain unprocessed, while color-distorted images undergo correction via histogram shifting. The complete system is deployed on smartphones. Experimental results show that AWBDILNet achieves a processing rate of 45.0255 Hz, which is 35.75% higher than that of the best-performing traditional method, ShuffleNet-v1. The AWBDILNet classifier effectively reduces computational complexity while maintaining high classification accuracy. Furthermore, it enables efficient and intelligent white balance correction on smartphones.