Design of a proximity-based motion gesture sensor using a single LED

다국어 초록 (Multilingual Abstract)

With the fast growth in popularity of portable handheld devices the daily participation of these devices such as tablets, media players, e-readers, and smart phones is becoming tremendously expanded which results human-machine interfaces (HMIs) to operate under various circumstances. Recently, these devices operate with complex functions that require complicated HMIs. Current HMIs can be classified into four types: touch-based, motion-based, vision-based, and proximity-based systems.
The touch-based system is one of the most common and natural methods either using fingers or a stylus. However, when the gloved, wet or dirty fingers touch, touching cannot be detected. The single-handed interaction is supported by motion-based system without touching or moving screen while interacting or by using an external controller. The vision-based system enables user to interface without touching the screen by using embedded camera and image processing. However it needs high power consumption and high computational cost. It is a huge drawback as a portable handheld device.
The proximity-based motion gesture sensor (MGS) system was proposed to overcome this obstacle. For low power dissipation and contactless gesture recognition, the system uses a proximity sensor assembled by a photodiodes (PD) and two IR LEDs. According to the distance and the angle between an object and IR LEDs, the intensity of reflected IR lights varies. The simple gestures can be extracted with change of the intensity and gesture recognition algorithm. The system needs three separate placements for two IR LEDs and a proximity sensor, resulting in large form factor (FF) when defining FF as the boundary of sensing system. That can be a design limitation. In this thesis, we propose a novel proximity-based MGS system that assembled by a proximity sensor having two PDs and an off chip LED. The past method detects the difference of time between the received lights from two IR LEDs when an object moves one side from the other. However, for the minimum detection margin, the method needs a minimum distance between the two LEDs. The proposed optical block method divides the view angles of the two PDs for the light reflected by an object, and needs only a single LED. When the distance between a proximity sensor and an LED of the proposed system is 4mm, the distance between two LEDs of a conventional system is 40mm. By using proposed system, the form factor can be reduced to one tenth.
Also, low noise sensor to amplify input signal from embedded small photodiodes (180μm by 180μm) is required. For low noise sensor, filtering technique is very important to reduce ambient light noise and electrical device noise. Generally, conventional filtering technique to reduce noise is band-pass-filter (BPF). But, BPF has disadvantage such as large area, low linearity and reliability. To overcome disadvantage of BPF synchronous sample/filtering method was proposed. This technique overcome disadvantage of BPF such as large area, low linearity and reliability, but it can’t filter DC noise and harmonic noise of modulation frequency. In this thesis, new synchronous sample/filtering method with Correlated Double Sampling (CDS) is proposed that compensate disadvantage of conventional method. The proposed method can filter DC noise and even harmonic noise of modulation frequency. Performance of sensor can be improved as using the proposed filtering method.
The sensor chip was fabricated in CIS 0.18μm technology and the chip size is 1.2 mm by 1.7 mm. Test boards consist of sensor with embedded photodiodes, LED driver and FPGA board to detect gesture. MGS system was tested in various conditions such as optical block, distance between photodiode and object so on to verify motion gesture operation.

목차 (Table of Contents)

• 1. Introduction 1
• 1.1 Motivation 1
• 2. Conventional Human-Machine Interfaces 4
• 2.1 Touch based Motion Gesture Sensor (MGS) 6
• 2.1.1 Capacitive Touch Screen Panel (TSP) 6
• 2.1.2 Resistive Touch Screen Panel (TSP) 8
• 2.1.3 Surface Acoustic Wave Touch Screen Panel (TSP) 10
• 2.1.4 Infrared Touch Screen Panel (TSP) 11
• 2.2 Motion based Motion Gesture Sensor (MGS) 12
• 2.2.1 Detection System with Accelerometer 13
• 2.2.2 Detection System with Gyroscope 14
• 2.3 Vision based Motion Gesture Sensor (MGS) 15
• 2.4 Proximity based Motion Gesture Sensor (MGS) 17
• 3. Optical structures and analysis of proposed proximity-based motion gesture sensor 18
• 3.1 Conventional proximity-based motion gesture sensor 19
• 3.2 The basic configuration of the proposed proximity-based MGS system 21
• 3.3 Optical analysis of proposed MGS system 22
• 3.3.1 Basic configuration 22
• 3.3.2 Proposed configuration 25
• 3.3.3 Design constraint of proposed optical block 27
• 3.3.4 Optical simulation 30
• 4. Design of an Infra-red Intensity Sensor 35
• 4.1 Relationship between timing margin and noise 38
• 4.2 Fundamental noise mechanisms 40
• 4.2.1 Thermal Noise 42
• 4.2.2 Low frequency noise 43
• 4.2.3 Shot noise 44
• 4.2.4 Ambient light noise 46
• 4.2.5 A noise of photodiode 47
• 4.3 Photodiode 48
• 4.3.1 Basic principle of photodiode 48
• 4.3.2 Capacitance of a photodiode 49
• 4.4 Trans-impedance amplifier (TIA) 50
• 4.4.1 Performance parameters 50
• 4.4.2 Types of TIA design 52
• 4.4.3 SNR comparison of TIAs 58
• 4.5 Pre- amplifier 60
• 4.6 Programmable Gain Amplifier (PGA) 63
• 4.7 Demodulation and Filter Technique 67
• 4.7.1 Band Pass Filter 67
• 4.7.2 Sample and Hold filter 71
• 4.7.3 Proposed CDS+S/H filter 73
• 4.7.4 Low pass filter (LPF) 76
• 4.7.5 Voltage controlled oscillator (VCO) 77
• 5.Simulation and Fabrication Results 79
• 5.1 HSPICE simulation results 80
• 5.2 Fabrication results 89
• 6.Test Results 90
• 6.1 Test environment 90
• 6.2 Test results 92
• 6.2.1 Test results of the Pre-AMP Stage 92
• 6.2.2 Test results of the PGA 93
• 6.2.3 Test results of the CDS+S/H filter 94
• 6.2.4 Test results of LPF 95
• 6.2.5 Test results of the proximity 96
• 6.2.6 Test results of the timing margin 97
• 6.2.7 Test results of the detection rate 103
• 7. Conclusion 106
• References 109