A Fully-Differential Switched-Capacitor Dual-Slope Capacitance-To-Digital Converter (CDC) for a Capacitive Pressure...

Area of Science:

• Electrical Engineering
• Sensor Technology
• Micro-Electro-Mechanical-Systems (MEMS)

Background:

• Optimal readout circuit design requires in-depth understanding of physical sensor behavior.
• Internet of Things (IoT) sensors prioritize low power consumption and moderate resolution over high-end performance.
• Basic Micro-Electro-Mechanical-System (MEMS) physics are crucial for developing effective sensor models.

Purpose of the Study:

• To propose and validate a Switched-Capacitor Dual-Slope based Capacitive-to-Digital Converter (CDC) for ambient air pressure sensing.
• To investigate the performance scaling potential and dead zones of the proposed CDC.
• To present an ambient air pressure sensor model for SPICE circuit simulators based on MEMS physics.

Main Methods:

• A Switched-Capacitor Dual-Slope based CDC architecture was designed and implemented.
• A measurement setup utilizing a real pressure sensor and an on-chip dummy MEMS bridge was employed for validation.
• A SPICE model for ambient air pressure sensors was developed based on MEMS physics.

Main Results:

• The converter concept was successfully proven on silicon in a 0.13 μm process.
• A 13-bit resolution was achieved within a 3.2 ms measurement time.
• The circuit demonstrated low power consumption (35 μA at 1.5 V) and a small occupied area (0.148 mm²).

Conclusions:

• The proposed CDC offers a viable solution for low-power, moderate-resolution sensing applications like IoT devices.
• The study highlights the importance of MEMS physics for accurate sensor modeling and readout circuit design.
• Future improvements could involve hybrid solutions for enhanced performance, as suggested by state-of-the-art comparisons.