Advances in Intelligent Nano-Micro-Scale Sensors and Actuators: Moving toward Self-Sustained Edge AI Microsystems.

Area of Science:

• Microsystems Engineering
• Artificial Intelligence
• Photonics

Background:

• Evolution of microelectromechanical systems (MEMS) towards integrated, self-powered sensors.
• Increasing demand for maintenance-free Internet-of-Things (IoT) systems driving energy harvesting integration.
• Advancements in artificial intelligence (AI) enabling edge computing for intelligent sensor platforms (AIoT).

Purpose of the Study:

• To explore the integration of energy harvesting, AI, and photonics in microsensors.
• To highlight the development of self-powered, intelligent sensing platforms.
• To discuss the potential of hybrid systems for advanced data interpretation and human-machine interaction.

Main Methods:

• Utilizing piezoelectric and triboelectric transducers for energy harvesting.
• Integrating AI at the sensor edge for real-time analytics and decision-making.
• Leveraging advances in mid-infrared, terahertz, and near-infrared photonics for sensitive detection and in-sensor computation.

Main Results:

• Development of self-sustained IoT deployments with enhanced network autonomy.
• Creation of intelligent AIoT systems capable of multimodal analytics and adaptive decision-making.
• Emergence of hybrid systems combining flexible electronics with photonic platforms for signal transduction and interpretation.

Conclusions:

• Highly integrated, self-sustained edge-AI microsystems are on the horizon.
• These systems promise perpetual, real-time intelligence and seamless user-environment interaction.
• Progress in materials, packaging, and co-design is crucial for reliability in harsh environments.