실리콘 기판 위에 효율적으로 집적한 기압 센서와 FET형 가스 센서

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 이종호.Sensor technology is becoming increasingly important to improve the quality of human life. Especially, various kinds of sensor technology have become essential due to increasing demand for smart mobile devices, automobiles and household appliances. Furthermore, as many types of sensors are installed on smart devices, it is more important to integrate different sensors in the IoT era. If multiple types of sensors are efficiently integrated with CMOS circuit on a single substrate, the footprint and power consumption could be reduced. Gas sensors are not only for detecting harmful gases, but also for improving indoor air quality and detecting diseases. The conventional resistor-type gas sensors have a simple structure and a simple manufacturing process, but they are large in size and have high power consumption. On the other hand, FET-type gas sensors can be fabricated very small in size and compatibly integrated with CMOS circuits, and they are easy to integrate with other types of sensors. In addition, built-in localized micro-heater can minimize power consumption of the FET-type gas sensors. In this dissertation, barometric pressure sensors and Si FET-type gas sensors are efficiently integrated on the same Si substrate using conventional MOSFET fabrication process. The barometric pressure sensors have built-in temperature sensors to accurately measure the atmospheric pressure according to the ambient temperature. In addition, the FET-type gas sensor has a localized micro-heater capable of heating up to 124 ºC with a power of 4 mW. NO2 gas sensing is successfully achieved with this gas sensor. Air-gap with a depth of 2.5 μm are formed in the Si substrate and used as the cavity for the barometric pressure sensor and as an insulating layer for the FET-type gas sensor. In addition, poly-Si with Boron ion implantation is used as the piezo-resistors of the barometric pressure sensor, the electrode of the temperature sensor, and the FG and micro-heater of the FET-type gas sensor at the same time. In this way, the barometric pressure sensors and the FET-type gas sensors are efficiently integrated using CMOS compatible fabrication process. The barometric pressure sensor has a built-in temperature sensor that can measure ambient temperature and atmospheric pressure at the same time. The measured atmospheric pressure varies with ambient temperature, but with a designed neural network, accurate atmospheric pressure can be obtained with an accuracy of 97.5 %.사물 인터넷 (IoT) 시대를 맞이하여 삶의 질을 개선하기 위한 센서 기술들이 점차 중요해지고 있다. 특히, 각종 스마트 기기들을 비롯한 자동차 및 가전 제품에 대한 센서 기술들이 필수적이 되고 있다. 아울러, 다양한 종류의 센서들의 통합 및 집적 기술이 주목받고 있다. 여러 유형의 센서들을 단일 기판에서 CMOS 회로와 효율적으로 통합하면 전력 소모를 줄일 수 있으며, 제조 단가 또한 낮출 수 있다. 다양한 센서기술 중 가스 센서는 유해 가스 감지뿐만 아니라 실내 공기 질 개선 및 질병 감지에 사용될 것으로 예상된다. 종래의 저항 형 가스 센서는 구조가 간단하며 제조 공정이 단순하지만 크기가 크고 전력 소비가 높은 편이다. 한편, FET 형 가스 센서는 매우 작은 크기로 제작이 가능하며 CMOS 회로와 호환 가능하다. 또한 내장된 마이크로 히터 (Micro-Heater)를 사용하게 되면 FET 형 가스 센서의 전력 소비를 최소화 시킬 수 있다. 본 논문에서는 기압 센서와 Si FET 형 가스 센서를 MOSFET 제조 공정기술을 사용하여 단일 실리콘 (Silicon) 기판에 효율적으로 집적하였다. 제작된 기압 센서는 온도 센서를 내장하고 있어서 주변 온도에 따른 대기압을 정확하게 측정 가능하다. 또한 FET 형 가스 센서는 4 mW의 전력으로 최대 124 ˚C까지 가열 할 수 있는 국부화 된 마이크로 히터를 내장하고 있다. 이 가스 센서로 이산화 질소가스 (NO2)의 농도를 측정하였다. 2.5 μm 깊이의 에어 갭 (Air-gap)을 Si 기판에 형성하고 이 에어 갭은 기압 센서의 공동 (Cavity) 및 FET 형 가스 센서의 절연 층으로 사용하였다. 또한, 붕소(Boron) 이온을 주입한 다결정 실리콘 (Poly-Si)은 기압 센서 및 온도 센서의 전극, FET 형 가스 센서의 플로팅 게이트 (Floating-gate), 그리고 마이크로 히터의 전극으로 동시에 사용하였다. 이러한 방식으로 기압 센서, FET 형 가스센서는 CMOS 호환 제조 공정을 사용하여 효율적으로 단일기판에 집적하였다. 기압 센서는 주변 온도와 대기압을 동시에 측정 할 수 있는 온도 센서를 내장하고 있으며, 주변 온도에 따른 대기압을 신경망을 통하여 97.5 %의 정확도로 측정할 수 있다.Chapter 1. Introduction 1 1.1. Sensor technology 1 1.1.1. Various types of sensors 1 1.1.2. Conventional MEMS sensors 2 1.2. Barometric pressure sensors 5 1.2.1. MEMS barometric pressure sensors 5 1.2.2. Diaphragm of barometric pressure sensors 6 1.2.3. Cavity in barometric pressure sensors 7 1.2.4. Types of barometric pressure sensors 9 1.3. Gas sensors 12 1.3.1. Resistor-type gas sensors 12 1.3.2. FET-type gas sensors 13 1.3.3. Heater and air-gap in gas sensors 17 1.4. Integration of various types of sensors 21 1.5. Purpose of research 22 1.6. Dissertation outline 23 Chapter 2. Device structure and fabrication 24 2.1. Integration of different sensors 24 2.2. Structure of barometric pressure sensors 26 2.2.1. Air pocket of barometric pressure sensors 26 2.2.2. New design of piezo-resistor 28 2.3. Structure of FET-type gas sensors 32 2.3.1. Structure and layout of FET-type gas sensors 32 2.4. Device fabrication 35 2.4.1. Key fabrication process 35 2.4.2. Formation of sensing material on FET-type gas sensors 47 Chapter 3. Device characteristics 49 3.1. Characteristics of barometric pressure sensors 49 3.1.1. Device simulation 49 3.1.2. Measurement setup 56 3.1.3. Measurement results 59 3.2. Characteristics of temperature sensors and micro-heater 63 3.2.1. Temperature sensor and its characteristics 63 3.2.2. Micro-heater of the gas sensors 70 3.3. Characteristics of gas sensors 77 3.3.1. I-V characteristics and nonvolatile functionality of FET-type gas sensors 77 3.3.2. Gas sensing mechanism 79 3.3.3. Gas measurement results 83 3.4. MLP neural network 86 Chapter 4. Conclusion 89 Bibliography 91 Abstract in Korean 100Docto

A Stochastic Approach to Noise Modeling for Barometric Altimeters

The question whether barometric altimeters can be applied to accurately track human motions is still debated, since their measurement performance are rather poor due to either coarse resolution or drifting behavior problems. As a step toward accurate short-time tracking of changes in height (up to few minutes), we develop a stochastic model that attempts to capture some statistical properties of the barometric altimeter noise. The barometric altimeter noise is decomposed in three components with different physical origin and properties: a deterministic time-varying mean, mainly correlated with global environment changes, and a first-order Gauss-Markov (GM) random process, mainly accounting for short-term, local environment changes, the effects of which are prominent, respectively, for long-time and short-time motion tracking; an uncorrelated random process, mainly due to wideband electronic noise, including quantization noise. Autoregressive-moving average (ARMA) system identification techniques are used to capture the correlation structure of the piecewise stationary GM component, and to estimate its standard deviation, together with the standard deviation of the uncorrelated component. M-point moving average filters used alone or in combination with whitening filters learnt from ARMA model parameters are further tested in few dynamic motion experiments and discussed for their capability of short-time tracking small-amplitude, low-frequency motions

Studies on Sensor Aided Positioning and Context Awareness

This thesis studies Global Navigation Satellite Systems (GNSS) in combination with sensor systems that can be used for positioning and obtaining richer context information. When a GNSS is integrated with sensors, such as accelerometers, gyroscopes and barometric altimeters, valuable information can be produced for several applications; for example availability or/and performance of the navigation system can be increased. In addition to position technologies, GNSS devices are integrated more often with different types of technologies to fulfil several needs, e.g., different types of context recognition. The most common integrated devices are accelerometer, gyroscope, and magnetometer but also other sensors could be used.More specifically, this thesis presents sensor aided positioning with two satellite signals with altitude assistance. The method uses both pseudorange and Doppler measurements. The system is required to be stationary during the process and a source of altitude information, e.g., a MEMS barometer, is needed in addition to a basic GNSS receiver. Authentic pseudorange and Doppler measurements with simulated altitude were used used to test the algorithm. Results showed that normally the accuracy of couple of kilometers is acquired. Thesis also studies on what kind of errors barometric altimeter might encounter especially in personal positioning. The results show that barometers in differential mode provide highly accurate altitude solution (within tens of centimeters), but local disturbances in pressure need to be acknowledged in the application design. For example, heating, ventilating, and air conditioning in a car can have effect of few meters. Thus this could cause problems if the barometer is used as a altimeter for under meter-level positioning or navigation.We also explore methods for sensor aided GNSS systems for context recognition. First, the activity and environment recognition from mobile phone sensor and radio receiver data is investigated. The aim is in activity (e.g., walking, running, or driving a vehicle) and environment (e.g., street, home, or restaurant) detection. The thesis introduces an algorithm for user specific adaptation of the context model parameters using the feedback from the user, which can provide a confidence measure about the correctness of a classification. A real-life data collection campaign validate the proposed method. In addition, the thesis presents a concept for automated crash detection to motorcycles. In this concept, three different inertial measurement units are attached to the motorist’s helmet, torso of the motorist, and to the rear of the motor cycle. A maximum a posteriori classifier is trained to classify the crash and normal driving. Crash dummy tests were done by throwing the dummy from different altitudes to simulate the effect of crash to the motorist and real data is collected by driving the motorcycle. Preliminary results proved the potential of the proposed method could be applicable in real situations. In all the proposed systems in this thesis, knowledge of the context can help the positioning system, but also positioning system can help in determining the context