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Schmitt trigger (ST) circuits are widely used integrated circuit (IC) blocks with hysteretic input/output (I/O) characteristics. Like the I/O characteristics of a living neuron, STs reject noise and provide stability to systems that they are deployed in. Indeed, single-input/single-output (SISO) STs are likely candidates to be the core unit element in artificial neural networks (ANNs) due not only to their similar I/O characteristics but also to their low power consumption and small silicon footprints. This paper presents an accurate and detailed analysis and design of six widely used complementary metal-oxide-semiconductor (CMOS) SISO ST circuits. The hysteresis characteristics of these ST circuits were derived for hand calculations and compared to original design equations and simulation results. Simulations were carried out in a well-established, 0.35 μm/3.3 V, analog/mixed-signal CMOS process. Additionally, simulations were performed using a wide range of supplies and process variations, but only 3.3 V supply results are presented. Most of the new design equations provide better accuracy and insights, as broad assumptions of original derivations were avoided.

Many algorithms have been developed for complementary metal–oxide–semiconductor (CMOS) image sensors to speed up analogue‐to‐digital (A‐to‐D) conversion of captured images. However, there is no objective blind‐image quality metric available to compare and quantify the quality and effectiveness of these speed‐up algorithms. In this work, we developed a blind‐image quality and complexity metric for this purpose. The proposed metric relies on counting the successive zeros in a code histogram. The proposed metric is called the conversion complexity metric (CCM). The CCM is designed to quantify how complex, and to predict how much time and power consuming a captured image is for A‐to‐D conversion, mainly by integrating (ramp) type A‐to‐D converter used in column‐parallel architectures of a CMOS image sensor (CIS). The proposed metric, CCM, is tested for linearity, monotonicity, and sensitivity to many types of introduced distortion. The CCM is compared with other no‐reference and full‐reference image quality and complexity metrics. It accomplished, for brightness change distortion, 99% linearity and 316% sensitivity, providing a computationally efficient blind‐image quality metric that no other metrics provide for CIS to intelligently adjust and optimise on‐chip analogue and digital signal processing.

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