3D Time-of-flight distance measurement with custom - Universität ...

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SOLID-STATE IMAGE SENSING 67 (2) GaAs devices achieve higher cut-off frequencies than silicon devices, since electrons have a six times higher mobility in low doped GaAs than in low doped silicon (N=10 14 ). Temporal response - numerical examples The following numerical example shows for the different influences of both thermal diffusion and drift in an electric field on the overall carrier movement. Generally, thermal diffusion is a non-directed random movement process. In the configuration illustrated in Figure 3.14 (a), however, the free electron can only move in a plane parallel to the semiconductor surface. This is because it is prevented from moving towards the semiconductor bulk by the electrical field created by the biased MOS gate. Also it cannot move to the left, since it sees a potential barrier there to the left neighboring gate, which is biased at 0V. Hence, the only direction the electron is allowed to move is towards the right neighboring gate. The abrupt potential steps between the gates and the flat shape of the potential under the single gates are idealized conditions, chosen to illustrate the diffusion mechanism only. For the psubstrate doping of 4⋅10 14 cm -3 of the process we used, we have an electron mobility of about 1300 cm 2 /Vs. With the Boltzmann constant k=1.3807⋅10 -23 J/K and elementary charge q=1.6⋅10 -19 C, this leads to a diffusivity Dn of 33.6 cm 2 /s at T=300° K. Assuming a gate length of 10 µm (=0.001 cm) we obtain a mean diffusion time of 30 nanoseconds. For a gate length of 30µm, however, the mean diffusion time would already be 270 nanoseconds, since the diffusion time increases with the square of the distance to travel. potential MOS gates 0V 5V 10V 0V 5V 10V 0V 5V 10V xgate oxide potential xgate E potential xgate (a) Thermal diffusion (b) Drift in an electric field (c) Combination Figure 3.14 Transport mechanisms (II). For a linearly falling potential between two gates, as illustrated in Figure 3.14 (b), we obtain a constant electric field between the gates. Applying a potential E
68 CHAPTER 3 difference of only 1 V between the gates of 10 µm length, this field is 10 5 V/m, a value at which the saturation velocity is not yet reached. The drift in such an electric field would take the electron only about 0.8 nanoseconds. For 30 µm gate length the traveling time would be 7 nanoseconds for the same potential difference. Also, for a fixed potential difference, the E-field drift time increases with the square of the drift length. In reality the process of carrier movement is a superposition of both cases discussed above. However, the real potential distribution is also a superposition of both extreme cases sketched in Figure 3.14 (a+b), we do not have an abrupt potential step between two gates at different potential nor do we have a linearly falling potential distribution over the entire gate length. Generally, for typical doping concentrations, the horizontal electrical field in the semiconductor, caused by a potential gradient between two neighboring gates, the so-called fringing field, only extends to a relatively small area between the gates [BEY]. Therefore, for relatively long gates, the charge transport will be dominated by pure thermal diffusion and only for very short gates do the fringing fields have an influence. Figure 3.14 (c) shows a more realistic potential distribution. The estimation of the fringing field extension is relatively complex and also difficult to simulate, since it strongly depends on the semiconductor’s doping concentrations. Unfortunately semiconductor foundries keep these exact doping profiles secret. At least we know that there are additional impurities and implants, especially directly beneath the semiconductor surface (e.g. anti-punch through implants). Due to this lack of process-parameter knowledge and the complexity of available simulation tools, it appears to be more practical, in the framework of this dissertation, to measure rather than to simulate the actual speed performance. 3.1.4 Noise sources The performance of solid-state imagers is limited by several different noise sources. These can generally be divided into three classes: (1) photon shot noise, (2) photocharge conversion noise and (3) quantization noise. Shot noise (or quantum noise) describes the statistical Poisson-distributed nature of the arrival process of photons and the generation process of electron-hole pairs.
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3D Time-of-flight distance measurem
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To my parents
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Meinen Eltern
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II 4. Power budget and resolution l
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Abstract Since we are living in a t
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centimeter accuracy. With the singl
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X Eine elegantere Methode zur Entfe
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INTRODUCTION 1 1. Introduction One
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INTRODUCTION 3 light source observe
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INTRODUCTION 5 Propagation time, ho
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INTRODUCTION 7 Chapter 3 gives a sh
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OPTICAL TOF RANGE MEASUREMENT 9 2.
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DEMODULATION PIXELS IN CMOS/CCD 117
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IMAGING TOF RANGE CAMERAS 151 6. Im
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IMAGING TOF RANGE CAMERAS 155 6.1.2
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IMAGING TOF RANGE CAMERAS 157 gate
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IMAGING TOF RANGE CAMERAS 159 6.2 2
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IMAGING TOF RANGE CAMERAS 161 n+ di
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IMAGING TOF RANGE CAMERAS 163 pixel
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IMAGING TOF RANGE CAMERAS 165 Power
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IMAGING TOF RANGE CAMERAS 167 Delay
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IMAGING TOF RANGE CAMERAS 169 6.3 3
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IMAGING TOF RANGE CAMERAS 171 Gate
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IMAGING TOF RANGE CAMERAS 175 Figur
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IMAGING TOF RANGE CAMERAS 177 Final
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IMAGING TOF RANGE CAMERAS 179 #5 #6
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SUMMARY AND PERSPECTIVE 181 7. Summ
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SUMMARY AND PERSPECTIVE 183 into th
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SUMMARY AND PERSPECTIVE 185 or phas
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188 CHAPTER 8 8.2 Typical parameter
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190 CHAPTER 8 Spectral luminous int
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192 CHAPTER 8 MCD03S: Conditions SC
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194 CHAPTER 8 MCD06S: Measurement c
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196 [BRK] Brockhaus, “Naturwissen
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198 [KAI] I. Kaisto, et al., “Opt
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200 [SP1] T. Spirig et al., “The
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ACKNOWLEDGMENTS 203 Acknowledgments
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206 Publication list 1. R. Lange, P
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