Low Complexity System on Chip Design to Acquire Signals from MOS Gas Sensor Applications.

Area of Science:

• Electronics Engineering
• Chemical Sensing Technology
• Embedded Systems

Background:

• Analog signals from gas sensors are crucial for identifying Volatile Organic Compounds (VOCs).
• Efficient signal acquisition, treatment, and machine learning classification are needed for accurate VOC recognition.
• Field Programmable Gate Arrays (FPGAs) offer a suitable platform for implementing these complex processes.

Purpose of the Study:

• To propose a low-cost, high-performance sigma-delta Analog to Digital Converter (ADC).
• To enable real-time processing and classification of gas sensor signals using an FPGA-based system.
• To demonstrate the capability of parallel signal digitalization for enhanced gas sensing applications.

Main Methods:

• Design and implementation of an 11-bit effective number of bits (ENOB) sigma-delta ADC.
• Integration of the ADC with a Linux System on Chip (SoC) for real-time data acquisition and processing.
• Utilizing Low-Voltage Differential Signaling (LVDS) for efficient data transfer within the FPGA.

Main Results:

• Achieved an 11-bit ENOB, Signal-to-Noise Ratio (SNR) of 75.97 dB, and Spurious-Free Dynamic Range (SFDR) of 72.28 dB.
• Real-time visualization of processed gas sensor signals on screen.
• Demonstrated high-frequency operation enabling multiplexing of multiple analog signals with optimal resolution.

Conclusions:

• The developed low-cost ADC is effective for real-time Volatile Organic Compound (VOC) recognition.
• The FPGA and Linux SoC system provide a powerful environment for scientific signal treatment and parallel processing.
• The high-frequency ADC design facilitates efficient digitalization of multiple gas sensor signals concurrently.