Ionization versus displacement damage effects in proton irradiated CMOS sensors manufactured in deep submicron process

Proton irradiation effects have been studied on CMOS image sensors manufactured in a 0.18 μm technology dedicated to imaging. The ionizing dose and displacement damage effects were discriminated and localized thanks to 60Co irradiations and large photodiode reverse current measurements. The only degradation observed was a photodiode dark current increase. It was found that ionizing dose effects dominate this rise by inducing generation centers at the interface between shallow trench isolations and depleted silicon regions. Displacement damages are responsible for a large degradation of dark current non-uniformity. This work suggests that designing a photodiode tolerant to ionizing radiation can mitigate an important part of proton irradiation effects

Total dose evaluation of deep submicron CMOS imaging technology through elementary device and pixel array behavior analysis

Ionizing radiation effects on CMOS image sensors (CIS) manufactured using a 0.18 µm imaging technology are presented through the behavior analysis of elementary structures, such as field oxide FET, gated diodes, photodiodes and MOSFETs. Oxide characterizations appear necessary to understand ionizing dose effects on devices and then on image sensors. The main degradations observed are photodiode dark current increases (caused by a generation current enhancement), minimum size NMOSFET off-state current rises and minimum size PMOSFET radiation induced narrow channel effects. All these effects are attributed to the shallow trench isolation degradation which appears much more sensitive to ionizing radiation than inter layer dielectrics. Unusual post annealing effects are reported in these thick oxides. Finally, the consequences on sensor design are discussed thanks to an irradiated pixel array and a comparison with previous work is discussed

Multilevel RTS in proton irradiated CMOS image sensors manufactured in a deep submicron technology

A new automated method able to detect multilevel random telegraph signals (RTS) in pixel arrays and to extract their main characteristics is presented. The proposed method is applied to several proton irradiated pixel arrays manufactured using a 0.18um CMOS process dedicated to imaging. Despite the large proton energy range and the large fluence range used, similar exponential RTS amplitude distributions are observed. A mean maximum amplitude independent of displacement damage dose is extracted from these distributions and the number of RTS defects appears to scale well with total nonionizing energy loss. These conclusions allow the prediction of RTS amplitude distributions. The effect of electric field on RTS amplitude is also studied and no significant relation between applied bias and RTS amplitude is observed

Theoretical evaluation of MTF and charge collection efficiency in CCD and CMOS image sensor

Classical models used to calculate the Modulation Transfer function (MTF) of a solid-state image sensor generally use a sinusoidal type of illumination. The approach, described in this paper, consists in considering a point-source illumination to built a theoretical three dimensional model of the diffusion and the collection of photo-carriers created within the image sensor array. Fourier transform formalism is used for this type of illumination. Solutions allow to evaluate the spatial repartition of the charge density collected in the space charge region, i.e. to get the Pixel Response Function (PRF) formulation. PRF enables to calculate analytically both MTF and crosstalk at every needed wavelengths. The model can take into account a uniformly doped substrate and an epitaxial layer grown on a highly doped substrate. The built-in electric field induced by the EPI/Substrate doping gradient is also taken into account. For these configurations, MTF, charge collection efficiency and crosstalk proportion are calculated. The study is established in the case of photodiode pixel but it can be easily extended to pinned photodiode pixels and photogate pixels

Ionizing radiation effects on CMOS imagers manufactured in deep submicron process

We present here a study on both CMOS sensors and elementary structures (photodiodes and in-pixel MOSFETs) manufactured in a deep submicron process dedicated to imaging. We designed a test chip made of one 128×128-3T-pixel array with 10 µm pitch and more than 120 isolated test structures including photodiodes and MOSFETs with various implants and different sizes. All these devices were exposed to ionizing radiation up to 100 krad and their responses were correlated to identify the CMOS sensor weaknesses. Characterizations in darkness and under illumination demonstrated that dark current increase is the major sensor degradation. Shallow trench isolation was identified to be responsible for this degradation as it increases the number of generation centers in photodiode depletion regions. Consequences on hardness assurance and hardening-by-design are discussed

Plataforma integrada de dados de acidentes de viação para suporte a processos de aprendizagem automática

Integrated road accident data platform to support machine learning techniques Traffic accidents are one of the most important concerns of the world, since they result in numerous casualties, injuries, and fatalities each year, as well as significant economic losses. There are many factors that are responsible for causing road accidents. If these factors can be better understood and predicted, it might be possible to take measures to mitigate the damages and its severity. The purpose of this dissertation is to identify these factors using accident data from 2016 to 2019 from the district of Setúbal, Portugal. This work aims at developing models that can select a set of influential factors that may be used to classify the severity of an accident, supporting an analysis on the accident data. In addition, this study also proposes a predictive model for future road accidents based on past data. Various machine learning approaches are used to create these models. Supervised machine learning methods such as decision trees (DT), random forests (RF), logistic regression (LR) and naive bayes (NB) are used, as well as unsupervised machine learning techniques including DBSCAN and hierarchical clustering. Results show that a rule-based model using C5.0 algorithm is capable of accurately detecting the most relevant factors describing a road accident severity. Furthermore, the results of the predictive model suggests the RF model could be a useful tool for forecasting accident hotspots; Sumário: Os acidentes de trânsito são uma grande preocupação a nível mundial, uma vez que resultam em grandes números de vítimas, feridos e mortes por ano, como também perdas económicas significativas. Existem vários fatores responsáveis por causar acidentes rodoviários. Se pudermos compreender e prever melhor estes fatores, talvez seja possível tomar medidas para mitigar os danos e a sua gravidade. O objetivo desta dissertação é identificar estes fatores utilizando dados de acidentes de 2016 a 2019 do distrito de Setúbal, Portugal. Este trabalho tem como objetivo desenvolver modelos capazes de selecionar um conjunto de fatores influentes e que possam vir a ser utilizados para classificar a gravidade de um acidente, suportando uma análise aos dados de acidentes. Além disso, este estudo também propõe um modelo de previsão para futuros acidentes rodoviários com base em dados do passado. Várias abordagens de aprendizagem automática são usadas para criar esses modelos. Métodos de aprendizagem supervisionada, como árvores de decisão (DT), random forest (RF), regressão logística (LR) e naive bayes (NB), são usados, bem como técnicas de aprendizagem automática não supervisionada, incluindo DBSCAN e clustering hierárquico. Os resultados mostram que um modelo baseado em regras usando o algoritmo C5.0 é capaz de detetar com precisão os fatores mais relevantes que descrevem a gravidade de um acidente de viação. Além disso, os resultados do modelo preditivo sugerem que o modelo RF pode ser uma ferramenta útil para a previsão de acidentes

Displacement damage effects due to neutron and proton irradiations on CMOS image sensors manufactured in deep submicron technology

Displacement damage effects due to proton and neutron irradiations of CMOS image sensors dedicated to imaging are presented through the analysis of the dark current behavior in pixel arrays and isolated photodiodes. The mean dark current increase and the dark current nonuniformity are investigated. Dark current histogram observations are compared to damage energy distributions based on GEANT 4 calculations. We also discuss, through annealing analysis, which defects could be responsible for the dark current in CMOS image sensors

Similarities Between Proton and Neutron Induced Dark Current Distribution in CMOS Image Sensors

Several CMOS image sensors were exposed to neutron or proton beams (displacement damage dose range from 4 TeV/g to 1825 TeV/g) and their radiation-induced dark current distributions are compared. It appears that for a given displacement damage dose, the hot pixel tail distributions are very similar, if normalized properly. This behavior is observed on all the tested CIS designs (4 designs, 2 technologies) and all the tested particles (protons from 50 MeV to 500 MeV and neutrons from 14 MeV to 22 MeV). Thanks to this result, all the dark current distribution presented in this paper can be fitted by a simple model with a unique set of two factors (not varying from one experimental condition to another). The proposed normalization method of the dark current histogram can be used to compare any dark current distribution to the distributions observed in this work. This paper suggests that this model could be applied to other devices and/or irradiation conditions