정규원 교수

Gas sensor technology is widely utilized in various areas ranging from home security, environment and air pollution, to industrial production. It also hold great promise in non-invasive exhaled breath detection and an essential device in future internet of things. The past decade has witnessed giant advance in both fundamental research and industrial development of gas sensors, yet current efforts are being explored to achieve better selectivity, higher sensitivity and lower power consumption. T

Oxygen vacancies and adsorbed oxygen species on metal oxide surfaces play important roles in various fields. However, existing methods for manipulating surface oxygen require severe settings and are ineffective for repetitive manipulation. We present a method to manipulate the amount of surface oxygen by modifying the oxygen adsorption energy by electrically controlling the electron concentration of the metal oxide. The surface oxygen control ability of the method is verified using first-princip

Carbon monoxide (CO) poisoning can easily occur in industrial and domestic settings, causing headaches, loss of consciousness, or death from overexposure. Commercially available CO gas sensors consume high power (typically 38 mW), whereas low-power gas sensors using nanostructured materials with catalysts lack reliability and uniformity. A low-power (1.8 mW @ 392 °C), sensitive, selective, reliable, and practical CO gas sensor is presented. The sensor adopts floated WO₃ film as a sens

Low-power metal oxide (MOX)-based gas sensors are widely applied in edge devices. To reduce power consumption, nanostructured MOX-based sensors that detect gas at low temperatures have been reported. However, the fabrication process of these sensors is difficult for mass production, and these sensors are lack uniformity and reliability. On the other hand, MOX film-based gas sensors have been commercialized but operate at high temperatures and exhibit low sensitivity. Herein, commercially advanta

Artificial olfactory systems (AOSs) that mimic biological olfactory systems are of great interest. However, most existing AOSs suffer from high energy consumption levels and latency issues due to data conversion and transmission. In this work, an energy- and area-efficient AOS based on near-sensor computing is proposed. The AOS efficiently integrates an array of sensing units (merged field effect transistor (FET)-type gas sensors and amplifier circuits) and an AND-type nonvolatile memory (NVM) a

Enhancing sensor sensitivity and gas identification capabilities is essential for the broad application of gas sensors. Developing efficient transducing methods for sensors can be applied to a wide range of sensors. However, developing such methods for resistive sensors remains challenging. In this study, an operating method that enhances both sensitivity and gas identification capability in resistive gas sensors is presented. The sensor operation is divided into two phases: the reaction phase a

The principal component analysis (PCA) and deep neural network (DNN) are used to classify the gas types (reducing and oxidizing) and to identify the concentration of gases. The pMOSFET-type gas sensor is used to provide sensing data for learning. The gas sensor has 15-nm-thick ZnO as a sensing layer processed by atomic layer deposition (ALD). The sensing characteristics of NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and H <sub xmlns:m

The response characteristics of a field-effect transistor (FET)-type gas sensor are compared with those of the resistor-type gas sensor fabricated on the same Si substrate. Both types of gas sensors have the same sensing material prepared by the same process. Indium oxide (In <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> ) film is adopt