Emerging Trends in Mixed-Signal Circuit Design with a Case Study on Edge AI Systems

Mixed-signal integrated circuit (IC) design, which integrates analog and digital functionalities on a single substrate, has emerged as a key enabler of modern electronic systems, including artificial intelligence (AI), fifth- and sixth-generation (5G/6G) communications, biomedical sensing, and industrial Internet-of-Things (IoT) applications. This paper presents a comprehensive survey of recent and impactful trends in mixed-signal design, including AI-assisted analog layout and synthesis automation, ultra-low-power and subthreshold circuit techniques, advanced on-chip self-calibration methods, heterogeneous three-dimensional (3D) chiplet integration, and the development of neuromorphic and quantum-control interfaces. To contextualize these advancements, a detailed case study of a battery-operated wireless vibration-sensing node for predictive maintenance of motor bearings is presented. The proposed system integrates a microelectromechanical systems (MEMS) accelerometer, a low-noise instrumentation amplifier, a 16-bit successive-approximation-register (SAR) analog-to-digital converter (ADC), an edge-AI-enabled microcontroller executing a convolutional anomaly detection model, and a Bluetooth Low Energy (BLE) communication module. Key design considerations, including component selection, power budgeting, signal-chain optimization, and failure-mode analysis, are systematically addressed. Furthermore, quantitative comparisons of critical components—such as ADCs, phase-locked loops (PLLs), and system-on-chip (SoC) architectures—are presented through structured evaluation tables and power-versus-performance analysis. The results demonstrate that an optimized co-design of analog precision, digital processing, and embedded AI significantly reduces communication overhead by minimizing radio transmission duty cycles, enabling multi-year battery operation from a standard 1500 mAh cell.