Low-noise high-dynamic-range single-photon avalanche diodes with integrated PQAR circuit in a standard 55 nm BCD

Single-photon avalanche diode (SPAD) based sensors and systems enable a variety of applications in biomedical, automotive, consumer, and security domains. While several established standard technologies, which can facilitate the design of SPAD-based systems are already in existence, challenges remain for the development of deep sub-micron monolithic integration of circuits and SPADs. In this work, we present SPADs along with pixel circuits in a standard GF 55 nm BCDL process. Two different designs demonstrate the flexibility allowed by the technology for a variety of applications. Both shallow and deep junction SPADs present excellent noise performance of less than 1 cps/mu m(2) at 3 V excess bias. An integrated passive-quench active-recharge (PQAR) circuit is used in conjunction with the SPADs, which enables a dead time of less than 2 ns, easily allowing for high dynamic range applications that require > 100 Mcps such as quantum communication and information technologies. The deep and shallow junction SPADs demonstrate an afterpulsing probability of < 0.5 % and < 2 % at 3V excess bias, respectively. The dead time is adjustable through analog control of the active-recharge circuit, allowing for afterpulsing reduction to below 0.1 % while maintaining Mcps operation. The shallow junction, which has a breakdown voltage of about 18 V and a peak sensitivity at 430 nm is particularly interesting for applications requiring low supply voltage, whereas the deep SPAD, which demonstrates > 4 % photon detection probability (PDP) at 940 nm, can be implemented in LiDAR sensors that require enhanced sensitivity at near-infrared (NIR) wavelengths. The measured timing jitter of both SPADs is < 50 ps FWHM at 3 V excess bias and 780 nm.

Direct time-of-flight SPAD image sensors for light detection and ranging

Preethi Padmanabhan

Depth sensing is an increasingly important feature in many applications of consumer, automotive, augmented/virtual reality (AR-VR), space and bio-medical imaging. Long range, high depth resolution, high spatial resolution, and high frame rates are often conflicting requirements and difficult to be simultaneously achieved due to extreme operating conditions. Direct time-of-flight (DTOF) has evolved to becoming a powerful technique to perform light detection and ranging (LiDAR). Thanks to advances in low-jitter optical detectors, such as single-photon avalanche diodes (SPADs), and accurate chronometers like time-to-digital converters (TDCs), picosecond timing resolution is possible, thus enabling millimetric depth resolutions. High ambient light is an inevitable challenge in LiDAR applications, whose levels may exceed up to 100 klux on a bright sunny day, making it particularly challenging to detect a target submerged within an overwhelming noise floor. High ambient light operation can be accommodated by means of optical filtering, a higher laser power or temporal filtering techniques. Optical filtering is often restricted to a narrow, 10-50 nm bandwidth, insufficient at high ambient light levels. Higher laser power is not always possible, due to eye safety regulations and power constraints. Temporal filtering such as time gating and coincidence detection can thus be powerful tools to cope with high ambient light. This thesis focuses on the design of DTOF sensors for LiDAR. To that end, two SPAD-based DTOF sensors are designed. The first sensor is designed in a 3D-stacked 45/65 nm CMOS technology, thus, enabling a modular architecture where the module itself comprises of 8x16 pixels. With a 60 ps-resolution TDC at its core, the sensor provides centimetric accuracy up to 300 m range in free space. The second sensor, named Jatayu, advances the previous design by hosting 256x128 pixels, thereby, significantly improving on its spatial resolution. While retaining its modularity, Jatayu also enables multi-level coincidence detection and progressive time-gating to suppress background light. To the best of the authorâs knowledge, progressive gating has been implemented in a LiDAR for the first time in this thesis. Designed in a 3D-stacked 45/22 nm CMOS technology, the sensor achieves under 7 cm accuracy over 100 m ranging and 10 klux background light. With its capability of acquiring 128Â£128, 3D depth maps of high dynamic range scenes, Jatayu is highly suitable for a variety of imaging applications in many different scenarios.

EPFL2021

Contribution à l'étude des modes de rayonnement acoustique d'une structure

Olivier Schevin

Noise radiated by different industrial structures that surround us in daily life are more and more considered as environmental pollution. Standards defining a tolerable sound level for each of these noise sources are regularly called into question and respecting them becomes an additional constraint for manufacturers. The last decades have seen the increasing development of means used to fight these noise disturbances. The wide diversity of noises perceived as harmful has contributed to the progressive increase in specificity and efficiency of solutions proposed to reduce the disturbances. For some applications, passive solutions, based on the use of materials capable of absorbing or deviating acoustic or vibratory waves, have progressively been replaced by "active" solutions, based on the generation of an acoustic wave of opposite phase to the disturbing one radiated by the noise source. Power transformers are sources for which passive solutions (anti-noise walls) are usually expensive and not very efficient, particularly at low frequency. Furthermore, characteristics of noise radiated by transformers (low frequency tone noise) are such that they are particularly well suited for the implementation of an active solution. Typically, an active control system dedicated to transformers (feedforward) is composed of actuators, used to generate the anti-noise and usually located near the tank, sensors, used to measure the attenuation obtained and to provide a reference signal, and a controller used to drive the actuators as a function of the information collected by the sensors. When the sensors are microphones, they are usually moved away from the noise source, in such a way that they pick up acoustic pressure that is representative of the noise propagated far away. In the vicinity of the source, local acoustic phenomena can occur which are not propagated far away. A microphone located near the noise source would therefore pick up an acoustic pressure that did not necessarily represent the one that effectively exists far away, the area where we are in fact seeking to reduce the noise. These phenomena, frequently grouped under the term "nearfield" tend to decrease the performance of the control system, owing to the fact that the controller seeks in this case to reduce an acoustic pressure which is not representative of the noise to be reduced. In practice, the significant amount of wiring required as a result of the positioning of the microphones in the far field, has a non-negligible effect on the cost of the system. The possibility of bringing the sensors closer to the source is sufficiently advantageous to envisage to studying the feasibility and the resulting consequences. The present approach consists in representing the primary field radiated by a vibrating structure in terms of a set of "acoustic radiation modes". The use of radiation modes to characterise the behaviour of a structure has received increasing attention since the beginning of 90's, especially for active control applications. Usually, this approach consists in representing the radiated power in terms of a set of surface velocity distributions, called radiation modes, that have the property of radiating independently of each other. Radiation modes result from diagonalization of a discrete expression of the radiation operator. We propose here to study the consequences on the radiation modes of a structure from bringing the microphones closer to it. We will study how the acoustic field varies with the distance and how this can be used to obtain a model, the complexity of which is adapted to the observation distance.

EPFL2001

Bits from Photons

Feng Yang

The trends in the design of image sensors are to build sensors with low noise, high sensitivity, high dynamic range, and small pixel size. How can we benefit from pixels with small size and high sensitivity? In this dissertation, we study a new image sensor that is reminiscent of traditional photographic film. Each pixel in the sensor has a binary response, giving only a one-bit quantized measurement of the local light intensity. The response function of the image sensor is non-linear and similar to a logarithmic function, which makes the sensor suitable for high dynamic range imaging. We first formulate the oversampled binary sensing scheme as a parameter estimation problem based on quantized Poisson statistics. We show that, with a single-photon quantization threshold and large oversampling factors, the Cramér-Rao lower bound (CRLB) of the estimation variance approaches that of an ideal unquantized sensor, that is, as if there were no quantization in the sensor measurements. Furthermore, the CRLB is shown to be asymptotically achievable by the maximum likelihood estimator (MLE). By showing that the log-likelihood function is concave, we guarantee the global optimality of iterative algorithms in finding the MLE. We study the performance of the oversampled binary sensing scheme in presence of dark current noise. The noise model is an additive Bernoulli noise with a known parameter, and the noise only flips the binary output from "0" to "1". We show that the binary sensor is quite robust with respect to noise and its dynamic range is only slightly reduced. The binary sensor first benefits from the increasing of the oversampling factor and then suffers in term of dynamic range. We again use the MLE to estimate the light intensity. When the threshold is a single photon, we show that the log-likelihood function is still concave. Thus, the global optimality can be achieved. But for thresholds larger than "1", this property does not hold true. By proving that when the light intensity is piecewise-constant, the likelihood function is a strictly pseudoconcave function, we guarantee to find the optimal solution of the MLE using iterative algorithms for arbitrary thresholds. For the general linear light field model, the log-likelihood function is not even quasiconcave when thresholds are larger than "1". In this circumstance, we find an initial solution by approximating the light intensity field with a piecewise-constant model, and then we use Newton's method to refine the estimation using the exact model. We then examine one of the most important parameters in the binary sensor, i.e., the threshold used to generate binary values. We prove the intuitive result that large thresholds achieve better estimation performance for strong light intensities, while small thresholds work better for low light intensities. To make a binary sensor that works in a larger range of light intensities, we propose to design a threshold array containing multiple thresholds instead of a single threshold for the binary sensing. The criterion is to minimize the average CRLB which is a good approximation of the mean squared error (MSE). The performance analysis on the new binary sensor verifies the effectiveness of our design. Again, the MLE is used for reconstructing the light intensity field from the binary measurements. By showing that the log-likelihood function is concave for arbitrary threshold arrays, we ensure that the iterative algorithms can find the optimal solution of the MLE. Finally, we study the reconstruction problem for the binary image sensor under a generalized piecewise-constant light intensity field model, which is quite useful when parameters like oversampling factors are unknown. We directly estimate light exposure values, i.e., the number of photons hitting on each pixel. We assume that the light exposure values are piecewise-constant and we use the MLE for the reconstruction. This optimization problem is solved by iteratively working out two subproblems. The first one is to find the optimal light exposure value for each segment, given the optimal segmentation of the binary measurements. The second one is to find the optimal segmentation of the binary measurements given the optimal light exposure values for each segment. Several algorithms are provided for solving this optimization problem. Dynamic programming can obtain the optimal solution for 1-D signals, but the computation is quite heavy. To reduce the burden of computation, we propose a greedy algorithm and a method based on pruning of binary trees or quadtrees.

EPFL2012