Browsing by Author "Chou, Ting-I"

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A 1-V 2.6-mW Environmental Compensated Fully Integrated Nose-on-a-Chip
(2018-10-01)
Chou, Ting-I
;
Chang, Kwuang-Han
;
Jhang, Jia-Yin
;
Chiu, Shih-Wen
;
Wang, Guoxing
Miniature gas detection system is desirable for many applications. In this brief, we propose a fully integrated nose-on-a-chip for mobile phones and wearable devices. The chip has 36 on-chip sensors, a 36-channel adaptive interface with an integrated programmable amplifier, a four-channel frequency readout interface, one on-chip temperature sensor, a successive approximation analog-to-digital converter, a scalable learning kernel cluster, and a reduced instruction set computing core with low-voltage static random-access memory. This chip is fabricated using the TSMC 180-nm CMOS process and consumes 2.6 mW at 1 V. on-chip temperature and humidity sensors are included in this system for acquiring environmental information. An environmental compensation algorithm based on common principal components analysis is also implemented for high-accuracy odor classification. This fully integrated nose-on-a-chip provides promising results for odor detection and classification for use in mobile and wearable device applications.
Design of a 0.5V 1.68mW Nose-on-a-Chip for Rapid Screen of Chronic Obstructive Pulmonary Disease
(2016-01-01)
Chou, Ting-I
;
Chiu, Shih-Wen
;
Chang, Kwuang-Han
;
Chen, Yi-Ju
;
Tang, Chen-Ting
Chronic obstructive pulmonary disease (COPD) still lacks a rapid diagnosis strategy. In this paper, we propose a low-power nose-on-a-chip for rapid COPD screening. This chip is designed for implementation in a personal handheld device that detects patient breath for COPD diagnosis. The chip has 36 on-chip sensors, a 36-channel adaptive interface with an integrated programmable amplifier, a four-channel frequency readout interface, one on-chip temperature sensor, a two-channel successive approximation analog-to-digital converter, a scalable learning kernel cluster, and a reduced instruction set computing core with low-voltage static random-access memory. This chip is fabricated in 90 nm CMOS and consumes 1.68 mW at 0.5 V. In simulation, the system distinguished between undiseased and diseased patients with 90.82% accuracy for a set of diseases including COPD and asthma and exhibited 92.31% accuracy for identifying patients with COPD or asthma. The system classified severity levels of COPD under four labels (normal, mild, moderate, and severe) with 92.00% accuracy. Accordingly, this work provides a promising solution for the unmet medical need of rapid COPD screening.