In this paper, an optimized memristor emulator circuit is designed, by using nine MOSFET transistors and a ground capacitor. Our area- and power-optimized emulator circuit can be used for basic data storage and processing at the monitoring edge, in real-time applications. The memristor shows a nonlinear voltage–current relationship, but no multiplier circuit provides the memristor’s nonlinear characteristics. As a result, the proposed memristor emulator has a very low chip area. The memristor circuit is designed in LTSpice, using 16 nm and 45 nm CMOS technology parameters, and the operating voltage is ±0.9 V. In this research, the theoretical derivations are validated using the simulated results of the memristor emulator circuit using different frequencies, capacitors, and input voltages in SPICE simulations.

The rapid development of electronic material and sensing technology has enabled research to be conducted on liquid metal-based soft sensors. The application of soft sensors is widespread and has many applications in soft robotics, smart prosthetics, and human-machine interfaces, where these sensors can be integrated for precise and sensitive monitoring. Soft sensors can be easily integrated for soft robotic applications, where traditional sensors are incompatible with robotic applications as these types of sensors show large deformation and very flexible. These liquid-metal-based sensors have been widely used for biomedical, agricultural and underwater applications. In this research, we have designed and fabricated a novel soft sensor that yields microfluidic channel arrays embedded with liquid metal Galinstan alloy. First of all, the article presents different fabrication steps such as 3D modeling, printing, and liquid metal injection. Different sensing performances such as stretchability, linearity, and durability results are measured and characterized. The fabricated soft sensor demonstrated excellent stability and reliability and exhibited promising sensitivity with respect to different pressures and conditions.