On the accuracy in modelling the statistical distribution of Random Telegraph Noise Amplitude

The power consumption of digital circuits is proportional to the square of operation voltage and the demand for low power circuits reduces the operation voltage towards the threshold of MOSFETs. A weak voltage signal makes circuits vulnerable to noise and the optimization of circuit design requires modelling noise. Random Telegraph Noise (RTN) is the dominant noise for modern CMOS technologies and Monte Carlo modelling has been used to assess its impact on circuits. This requires statistical distributions of RTN amplitude and three different distributions were proposed by early works: Lognormal, Exponential, and Gumbel distributions. They give substantially different RTN predictions and agreement has not been reached on which distribution should be used, calling the modelling accuracy into questions. The objective of this work is to assess the accuracy of these three distributions and to explore other distributions for better accuracy. A novel criterion has been proposed for selecting distributions, which requires a monotonic reduction of modelling errors with increasing number of traps. The three existing distributions do not meet this criterion and thirteen other distributions are explored. It is found that the Generalized Extreme Value (GEV) distribution has the lowest error and meet the new criterion. Moreover, to reduce modelling errors, early works used bimodal Lognormal and Exponential distributions, which have more fitting parameters. Their errors, however, are still higher than those of the monomodal GEV distribution. GEV has a long distribution tail and predicts substantially worse RTN impact. The work highlights the uncertainty in predicting the RTN distribution tail by different statistical models

High-Density Solid-State Memory Devices and Technologies

This Special Issue aims to examine high-density solid-state memory devices and technologies from various standpoints in an attempt to foster their continuous success in the future. Considering that broadening of the range of applications will likely offer different types of solid-state memories their chance in the spotlight, the Special Issue is not focused on a specific storage solution but rather embraces all the most relevant solid-state memory devices and technologies currently on stage. Even the subjects dealt with in this Special Issue are widespread, ranging from process and design issues/innovations to the experimental and theoretical analysis of the operation and from the performance and reliability of memory devices and arrays to the exploitation of solid-state memories to pursue new computing paradigms

Using Matlab NeuralNetworkStart to classify RTN traces for security applications

This Master's thesis has focused on the study of Random Telegraph Noise (RTN) traces for the training of neural networks. Specifically, the randomness of RTN traces has been studied by means of a first conversion to bits and a subsequent validation, based on the uncertainty in the repetition of consecutive binary symbols. This study has been carried out at the simulation level, using the neural network model based on the RTN traces, by means of MATLAB's NNStart application

Characterisation and modelling of Random Telegraph Noise in nanometre devices

The power consumption of digital circuits is proportional to the square of operation voltage and the demand for low power circuits reduces the operation voltage towards the threshold of MOSFETs. A weak voltage signal makes circuits vulnerable to noise and the optimization of circuit design requires an accurate noise model. RTN is the dominant noise for modern CMOS technologies. This research focuses on the instability induced by Random Telegraph Noise (RTN) in nano-devices for low power applications, such as the Internet of Things (IoT). RTN is a stochastic noise that can be observed in the drain/gate current of a device when traps capture and emit electrons or holes. The impact of RTN instabilities in devices has been widely investigated. Although progress has been made, the understanding of RTN instabilities remains incomplete and many issues are unresolved. This work focuses on developing a statistical model for characterising, modelling and analysing of the impact of RTN on MOSFET performance, as well as to study the prediction for long-term RTN impact on real circuits. As transistor sizes are downscaled, a single trapped charge has a larger impact and RTN becomes increasingly important. To optimize circuit design, one needs to assess the impact of RTN on circuits, which can only be accomplished if there is an accurate statistical model of RTN. The dynamic Monte Carlo modelling requires the statistical distribution functions of both the amplitude and the capture/emission time (CET) of traps. Early works were focused on the amplitude distribution and the experimental data of CETs has been too limited to establish their statistical distribution reliably. In particular, the time window used has often been small, e.g. 10 sec or less, so that there is little data on slow traps. It is not known whether the CET distribution extracted from such a limited time window can be used to predict the RTN beyond the test time window. The first contribution of this work is three-fold: to provide long-term RTN data and use it to test the CET distributions proposed by early works; to propose a methodology for characterising the CET distribution for a fabrication process efficiently; and, for the first time, to verify the long-term prediction capability of a CET distribution beyond the time window used for its extraction. On the statistical distributions of RTN amplitude, three different distributions were proposed by early works: Lognormal, Exponential, and Gumbel distributions. They give substantially different RTN predictions and agreement has not been reached on which distribution should be used, calling the modelling accuracy into question. The second contribution of this work is to assess the accuracy of these three distributions and to explore other distributions for better accuracy. A novel criterion has been proposed for selecting distributions, which requires a monotonic reduction of modelling errors with increasing number of traps. The three existing distributions do not meet this criterion and thirteen other distributions are explored. It is found that the Generalized Extreme Value (GEV) distribution has the lowest error and meets the new criterion. Moreover, to reduce modelling errors, early works used bimodal Lognormal and Exponential distributions, which have more fitting parameters. Their errors, however, are still higher than those of the monomodal GEV distribution. GEV has a long distribution tail and predicts substantially worse RTN impact. The project highlights the uncertainty in predicting the RTN distribution tail by different statistical models. The last contribution of the project is studying the impact of different gate biases on RTN distributions. At two different gate voltage conditions: one close to threshold voltage |Vth| and the other under operating conditions, it is found that the RTN amplitude follows different distributions. At operating voltage condition, Lognormal distribution has the lowest error for RTN amplitude distribution in comparison with other distributions. The amplitude distribution at close to |Vth| has a longer tail compared with the distribution tail at operating voltage. However, RTN capture/emission time distribution is not impacted by gate bias and follows Log-uniform distribution

Particle Physics Reference Library

This second open access volume of the handbook series deals with detectors, large experimental facilities and data handling, both for accelerator and non-accelerator based experiments. It also covers applications in medicine and life sciences. A joint CERN-Springer initiative, the “Particle Physics Reference Library” provides revised and updated contributions based on previously published material in the well-known Landolt-Boernstein series on particle physics, accelerators and detectors (volumes 21A,B1,B2,C), which took stock of the field approximately one decade ago. Central to this new initiative is publication under full open access

Quantitative Thermography and Image Quality in Additive Manufacturing of Metal

This thesis presents work on quantitative thermography in the additive manufacturing of metals process. The work is motivated by a need for accurate, spatiotemporally resolved measurements of the thermal fields near the heat source, which is usually 50-500 µm in size. This level of detail requires a high spatial sampling rate, which can be provided by near infrared sensitive silicon-based instruments. The high spatial sampling rate means that the resolution of the instruments is limited by the imaging components. The imaging performance is characterised by the spatial transfer function. In this work three distinct silicon based thermographic instruments were designed and constructed. The three instruments were trialled in additive manufacturing of metals applications. The three trials were: a low-cost smart-phone-sensor system used on a commercial direct energy deposition machine; a high-performance sensor system with a telephoto lens used on a modified commercial machine; and a high performance, high magnification system used on a custom built process replicator. The performance of the three systems for their applications was assessed. The three instruments have provided valid research data which paves the way for future studies using these technologies. The instrument used for thermography on the process replicator could resolve previously unseen levels of thermal detail in the process, having an instantaneous field of view of 3 µm. The measurement field of view of this instrument was found to be a circle of 130 µm diameter. The cooling rates in the process replicator for the alloy (Ti-6-4). were measured to be 0.06- 0.14 °C µs -1 , which is consistent with literature for this material. The spatial transfer function of the instruments was calculated using methods developed for this thesis. Measurements of the spatial transfer function were used to reconstruct the thermal fields and a method for validating the reconstruction was devised. A reconstruction method devised for this work was found to outperform the standard reconstruction methods used in literature, for scenes similar to those found in the additive manufacture of metals

Topical Workshop on Electronics for Particle Physics

The purpose of the workshop was to present results and original concepts for electronics research and development relevant to particle physics experiments as well as accelerator and beam instrumentation at future facilities; to review the status of electronics for the LHC experiments; to identify and encourage common efforts for the development of electronics; and to promote information exchange and collaboration in the relevant engineering and physics communities

Telecommunication Systems

This book is based on both industrial and academic research efforts in which a number of recent advancements and rare insights into telecommunication systems are well presented. The volume is organized into four parts: "Telecommunication Protocol, Optimization, and Security Frameworks", "Next-Generation Optical Access Technologies", "Convergence of Wireless-Optical Networks" and "Advanced Relay and Antenna Systems for Smart Networks." Chapters within these parts are self-contained and cross-referenced to facilitate further study

Statistical Analysis of the Random Telegraph Noise in a 1.1 μm Pixel, 8.3 MP CMOS Image Sensor Using On-Chip Time Constant Extraction Method

A study of the random telegraph noise (RTN) of a 1.1 μm pitch, 8.3 Mpixel CMOS image sensor (CIS) fabricated in a 45 nm backside-illumination (BSI) technology is presented in this paper. A noise decomposition scheme is used to pinpoint the noise source. The long tail of the random noise (RN) distribution is directly linked to the RTN from the pixel source follower (SF). The full 8.3 Mpixels are classified into four categories according to the observed RTN histogram peaks. A theoretical formula describing the RTN as a function of the time difference between the two phases of the correlated double sampling (CDS) is derived and validated by measured data. An on-chip time constant extraction method is developed and applied to the RTN analysis. The effects of readout circuit bandwidth on the settling ratios of the RTN histograms are investigated and successfully accounted for in a simulation using a RTN behavior model