Advances of semiconductor gas sensor on multi-parameter sensing and features extraction methods

Area of Science:

• Materials Science
• Chemical Sensing
• Data Science

Background:

• Growing demand for precise gas detection in complex environments.
• Semiconductor gas sensors are critical for industrial, medical, and environmental monitoring.
• Recent years have seen significant advancements in sensor technology.

Purpose of the Study:

• Systematically review progress in semiconductor gas sensor technology.
• Focus on multi-parameter sensing, drift compensation, and feature extraction.
• Outline future research directions for enhanced gas detection.

Main Methods:

• Analysis of multi-parameter data acquisition techniques (sensor arrays, temperature/optical modulation).
• Summary of drift compensation strategies (calibration, machine learning, deep learning).
• Evaluation of feature extraction approaches (time-domain analysis, deep learning).

Main Results:

• Multi-parameter sensing enhances discrimination accuracy to over 98% for complex gas mixtures.
• Deep learning drift compensation maintains over 90% classification accuracy under drift.
• Feature extraction techniques achieve excellent gas recognition performance.

Conclusions:

• Multi-parameter sensing, drift compensation, and feature extraction are key to advanced semiconductor gas sensors.
• Machine learning, particularly deep learning, significantly improves sensor adaptability and accuracy.
• Future work should focus on hybrid platforms, adaptive correction, and explainable AI.