A statistical study of time dependent reliability degradation of nanoscale MOSFET devices

Charge trapping at the channel interface is a fundamental issue that adversely affects the reliability of metal-oxide semiconductor field effect transistor (MOSFET) devices. This effect represents a new source of statistical variability as these devices enter the nano-scale era. Recently, charge trapping has been identified as the dominant phenomenon leading to both random telegraph noise (RTN) and bias temperature instabilities (BTI). Thus, understanding the interplay between reliability and statistical variability in scaled transistors is essential to the implementation of a ‘reliability-aware’ complementary metal oxide semiconductor (CMOS) circuit design. In order to investigate statistical reliability issues, a methodology based on a simulation flow has been developed in this thesis that allows a comprehensive and multi-scale study of charge-trapping phenomena and their impact on transistor and circuit performance. The proposed methodology is accomplished by using the Gold Standard Simulations (GSS) technology computer-aided design (TCAD)-based design tool chain co-optimization (DTCO) tool chain. The 70 nm bulk IMEC MOSFET and the 22 nm Intel fin-shape field effect transistor (FinFET) have been selected as targeted devices. The simulation flow starts by calibrating the device TCAD simulation decks against experimental measurements. This initial phase allows the identification of the physical structure and the doping distributions in the vertical and lateral directions based on the modulation in the inversion layer’s depth as well as the modulation of short channel effects. The calibration is further refined by taking into account statistical variability to match the statistical distributions of the transistors’ figures of merit obtained by measurements. The TCAD simulation investigation of RTN and BTI phenomena is then carried out in the presence of several sources of statistical variability. The study extends further to circuit simulation level by extracting compact models from the statistical TCAD simulation results. These compact models are collected in libraries, which are then utilised to investigate the impact of the BTI phenomenon, and its interaction with statistical variability, in a six transistor-static random access memory (6T-SRAM) cell. At the circuit level figures of merit, such as the static noise margin (SNM), and their statistical distributions are evaluated. The focus of this thesis is to highlight the importance of accounting for the interaction between statistical variability and statistical reliability in the simulation of advanced CMOS devices and circuits, in order to maintain predictivity and obtain a quantitative agreement with a measured data. The main findings of this thesis can be summarised by the following points: Based on the analysis of the results, the dispersions of VT and ΔVT indicate that a change in device technology must be considered, from the planar MOSFET platform to a new device architecture such as FinFET or SOI. This result is due to the interplay between a single trap charge and statistical variability, which has a significant impact on device operation and intrinsic parameters as transistor dimensions shrink further. The ageing process of transistors can be captured by using the trapped charge density at the interface and observing the VT shift. Moreover, using statistical analysis one can highlight the extreme transistors and their probable effect on the circuit or system operation. The influence of the passgate (PG) transistor in a 6T-SRAM cell gives a different trend of the mean static noise margin

Reliability in the face of variability in nanometer embedded memories

In this thesis, we have investigated the impact of parametric variations on the behaviour of one performance-critical processor structure - embedded memories. As variations manifest as a spread in power and performance, as a first step, we propose a novel modeling methodology that helps evaluate the impact of circuit-level optimizations on architecture-level design choices. Choices made at the design-stage ensure conflicting requirements from higher-levels are decoupled. We then complement such design-time optimizations with a runtime mechanism that takes advantage of adaptive body-biasing to lower power whilst improving performance in the presence of variability. Our proposal uses a novel fully-digital variation tracking hardware using embedded DRAM (eDRAM) cells to monitor run-time changes in cache latency and leakage. A special fine-grain body-bias generator uses the measurements to generate an optimal body-bias that is needed to meet the required yield targets. A novel variation-tolerant and soft-error hardened eDRAM cell is also proposed as an alternate candidate for replacing existing SRAM-based designs in latency critical memory structures. In the ultra low-power domain where reliable operation is limited by the minimum voltage of operation (Vddmin), we analyse the impact of failures on cache functional margin and functional yield. Towards this end, we have developed a fully automated tool (INFORMER) capable of estimating memory-wide metrics such as power, performance and yield accurately and rapidly. Using the developed tool, we then evaluate the #effectiveness of a new class of hybrid techniques in improving cache yield through failure prevention and correction. Having a holistic perspective of memory-wide metrics helps us arrive at design-choices optimized simultaneously for multiple metrics needed for maintaining lifetime requirements

Circuit-level modelling and simulation of carbon nanotube devices

The growing academic interest in carbon nanotubes (CNTs) as a promising novel class of electronic materials has led to significant progress in the understanding of CNT physics including ballistic and non-ballistic electron transport characteristics. Together with the increasing amount of theoretical analysis and experimental studies into the properties of CNT transistors, the need for corresponding modelling techniques has also grown rapidly. This research is focused on the electron transport characteristics of CNT transistors, with the aim to develop efficient techniquesto model and simulate CNT devices for logic circuit analysis.The contributions of this research can be summarised as follows. Firstly, to accelerate the evaluation of the equations that model a CNT transistor, while maintaining high modelling accuracy, three efficient numerical techniques based on piece-wise linear, quadratic polynomial and cubic spline approximation have been developed. The numerical approximation simplifies the solution of the CNT transistor’s self-consistent voltage such that the calculation of the drain-source current is accelerated by at least two orders of magnitude. The numerical approach eliminates complicated calculations in the modelling process and facilitates the development of fast and efficient CNT transistor models for circuit simulation.Secondly, non-ballistic CNT transistors have been considered, and extended circuit-level models which can capture both ballistic and non-ballistic electron transport phenomena, including elastic scattering, phonon scattering, strain and tunnelling effects, have been developed. A salient feature of the developed models is their ability to incorporate both ballistic and non-ballistic transport mechanisms without a significant computational cost. The developed models have been extensively validated against reported transport theories of CNT transistors and experimental results.Thirdly, the proposed carbon nanotube transistor models have been implemented on several platforms. The underlying algorithms have been developed and tested in MATLAB, behaviourallevel models in VHDL-AMS, and improved circuit-level models have been implemented in two versions of the SPICE simulator. As the final contribution of this work, parameter variation analysis has been carried out in SPICE3 to study the performance of the proposed circuit-level CNT transistor models in logic circuit analysis. Typical circuits, including inverters and adders, have been analysed to determine the dependence of the circuit’s correct operation on CNT parameter variation

Gestión de jerarquías de memoria híbridas a nivel de sistema

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadoras y Automática y de Ku Leuven, Arenberg Doctoral School, Faculty of Engineering Science, leída el 11/05/2017.In electronics and computer science, the term ‘memory’ generally refers to devices that are used to store information that we use in various appliances ranging from our PCs to all hand-held devices, smart appliances etc. Primary/main memory is used for storage systems that function at a high speed (i.e. RAM). The primary memory is often associated with addressable semiconductor memory, i.e. integrated circuits consisting of silicon-based transistors, used for example as primary memory but also other purposes in computers and other digital electronic devices. The secondary/auxiliary memory, in comparison provides program and data storage that is slower to access but offers larger capacity. Examples include external hard drives, portable flash drives, CDs, and DVDs. These devices and media must be either plugged in or inserted into a computer in order to be accessed by the system. Since secondary storage technology is not always connected to the computer, it is commonly used for backing up data. The term storage is often used to describe secondary memory. Secondary memory stores a large amount of data at lesser cost per byte than primary memory; this makes secondary storage about two orders of magnitude less expensive than primary storage. There are two main types of semiconductor memory: volatile and nonvolatile. Examples of non-volatile memory are ‘Flash’ memory (sometimes used as secondary, sometimes primary computer memory) and ROM/PROM/EPROM/EEPROM memory (used for firmware such as boot programs). Examples of volatile memory are primary memory (typically dynamic RAM, DRAM), and fast CPU cache memory (typically static RAM, SRAM, which is fast but energy-consuming and offer lower memory capacity per are a unit than DRAM). Non-volatile memory technologies in Si-based electronics date back to the 1990s. Flash memory is widely used in consumer electronic products such as cellphones and music players and NAND Flash-based solid-state disks (SSDs) are increasingly displacing hard disk drives as the primary storage device in laptops, desktops, and even data centers. The integration limit of Flash memories is approaching, and many new types of memory to replace conventional Flash memories have been proposed. The rapid increase of leakage currents in Silicon CMOS transistors with scaling poses a big challenge for the integration of SRAM memories. There is also the case of susceptibility to read/write failure with low power schemes. As a result of this, over the past decade, there has been an extensive pooling of time, resources and effort towards developing emerging memory technologies like Resistive RAM (ReRAM/RRAM), STT-MRAM, Domain Wall Memory and Phase Change Memory(PRAM). Emerging non-volatile memory technologies promise new memories to store more data at less cost than the expensive-to build silicon chips used by popular consumer gadgets including digital cameras, cell phones and portable music players. These new memory technologies combine the speed of static random-access memory (SRAM), the density of dynamic random-access memory (DRAM), and the non-volatility of Flash memory and so become very attractive as another possibility for future memory hierarchies. The research and information on these Non-Volatile Memory (NVM) technologies has matured over the last decade. These NVMs are now being explored thoroughly nowadays as viable replacements for conventional SRAM based memories even for the higher levels of the memory hierarchy. Many other new classes of emerging memory technologies such as transparent and plastic, three-dimensional(3-D), and quantum dot memory technologies have also gained tremendous popularity in recent years...En el campo de la informática, el término ‘memoria’ se refiere generalmente a dispositivos que son usados para almacenar información que posteriormente será usada en diversos dispositivos, desde computadoras personales (PC), móviles, dispositivos inteligentes, etc. La memoria principal del sistema se utiliza para almacenar los datos e instrucciones de los procesos que se encuentre en ejecución, por lo que se requiere que funcionen a alta velocidad (por ejemplo, DRAM). La memoria principal está implementada habitualmente mediante memorias semiconductoras direccionables, siendo DRAM y SRAM los principales exponentes. Por otro lado, la memoria auxiliar o secundaria proporciona almacenaje(para ficheros, por ejemplo); es más lenta pero ofrece una mayor capacidad. Ejemplos típicos de memoria secundaria son discos duros, memorias flash portables, CDs y DVDs. Debido a que estos dispositivos no necesitan estar conectados a la computadora de forma permanente, son muy utilizados para almacenar copias de seguridad. La memoria secundaria almacena una gran cantidad de datos aun coste menor por bit que la memoria principal, siendo habitualmente dos órdenes de magnitud más barata que la memoria primaria. Existen dos tipos de memorias de tipo semiconductor: volátiles y no volátiles. Ejemplos de memorias no volátiles son las memorias Flash (algunas veces usadas como memoria secundaria y otras veces como memoria principal) y memorias ROM/PROM/EPROM/EEPROM (usadas para firmware como programas de arranque). Ejemplos de memoria volátil son las memorias DRAM (RAM dinámica), actualmente la opción predominante a la hora de implementar la memoria principal, y las memorias SRAM (RAM estática) más rápida y costosa, utilizada para los diferentes niveles de cache. Las tecnologías de memorias no volátiles basadas en electrónica de silicio se remontan a la década de1990. Una variante de memoria de almacenaje por carga denominada como memoria Flash es mundialmente usada en productos electrónicos de consumo como telefonía móvil y reproductores de música mientras NAND Flash solid state disks(SSDs) están progresivamente desplazando a los dispositivos de disco duro como principal unidad de almacenamiento en computadoras portátiles, de escritorio e incluso en centros de datos. En la actualidad, hay varios factores que amenazan la actual predominancia de memorias semiconductoras basadas en cargas (capacitivas). Por un lado, se está alcanzando el límite de integración de las memorias Flash, lo que compromete su escalado en el medio plazo. Por otra parte, el fuerte incremento de las corrientes de fuga de los transistores de silicio CMOS actuales, supone un enorme desafío para la integración de memorias SRAM. Asimismo, estas memorias son cada vez más susceptibles a fallos de lectura/escritura en diseños de bajo consumo. Como resultado de estos problemas, que se agravan con cada nueva generación tecnológica, en los últimos años se han intensificado los esfuerzos para desarrollar nuevas tecnologías que reemplacen o al menos complementen a las actuales. Los transistores de efecto campo eléctrico ferroso (FeFET en sus siglas en inglés) se consideran una de las alternativas más prometedores para sustituir tanto a Flash (por su mayor densidad) como a DRAM (por su mayor velocidad), pero aún está en una fase muy inicial de su desarrollo. Hay otras tecnologías algo más maduras, en el ámbito de las memorias RAM resistivas, entre las que cabe destacar ReRAM (o RRAM), STT-RAM, Domain Wall Memory y Phase Change Memory (PRAM)...Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Mixed-signal integrated circuits design and validation for automotive electronics applications

Automotive electronics is a fast growing market. In a field primarily dominated by mechanical or hydraulic systems, over the past few decades there has been exponential growth in the number of electronic components incorporated into automobiles. Partly thanks to the advance in high voltage smart power processes in nowadays cars is possible to integrate both power/high voltage electronics and analog/digital signal processing circuitry thus allowing to replace a lot of mechanical systems with electro-mechanical or fully electronic ones. High level modeling of complex electronic systems is gaining importance relatively to design space exploration, enabling shorter design and verification cycles, allowing reduced time-to-market. A high level model of a resistor string DAC to evaluate nonlinearities has been developed in MATLAB environment. As a test case for the model, a 10 bit resistive DAC in 0.18um is designed and the results were compared with the traditional transistor level approach. Then we face the analysis and design of a fundamental block: the bandgap voltage reference. Automotive requirements are tough, so the design of the voltage reference includes a pre-regulation part of the battery voltage that allows to enhance overall performances. Moreover an analog integrated driver for an automotive application whose architecture exploits today’s trends of analog-digital integration allowing a greater range of flexibility allowing high configurability and fast prototipization is presented. We covered also the mixed-signal verification approach. In fact, as complexity increases and mixed-signal systems become more and more pervasive, test and verification often tend to be the bottleneck in terms of time effort. A complete flow for mixed-signal verification using VHDL-AMS modeling and Python scripting is presented as an alternative to complex transistor level simulations. Finally conclusions are drawn

Synaptic weight modification and storage in hardware neural networks

In 2011 the International Technology Roadmap for Semiconductors, ITRS 2011, outlined how the semiconductor industry should proceed to pursue Moore’s Law past the 18nm generation. It envisioned a concept of ‘More than Moore’, in which existing semiconductor technologies can be exploited to enable the fabrication of diverse systems and in particular systems which integrate non-digital and biologically based functionality. A rapid expansion and growing interest in the fields of microbiology, electrophysiology, and computational neuroscience occurred. This activity has provided significant understanding and insight into the function and structure of the human brain leading to the creation of systems which mimic the operation of the biological nervous system. As the systems expand a need for small area, low power devices which replicate the important biological features of neural networks has been established to implement large scale networks. In this thesis work is presented which focuses on the modification and storage of synaptic weights in hardware neural networks. Test devices were incorporated on 3 chip runs; each chip was fabricated in a 0.35μm process from Austria MicroSystems (AMS) and used for parameter extraction, in accordance with the theoretical analysis presented. A compact circuit is presented which can implement STDP, and has advantages over current implementations in that the critical timing window for synaptic modification is implemented within the circuit. The duration of the critical timing window is set by the subthreshold current controlled by the voltage, Vleak, applied to transistor Mleak in the circuit. A physical model to predict the time window for plasticity to occur is formulated and the effects of process variations on the window is analysed. The STDP circuit is implemented using two dedicated circuit blocks, one for potentiation and one for depression where each block consists of 4 transistors and a polysilicon capacitor, and an area of 980µm2. SpectreS simulations of the back-annotated layout of the circuit and experimental results indicate that STDP with biologically plausible critical timing windows over the range 10µs to 100ms can be implemented. Theoretical analysis using parameters extracted from MOS test devices is used to describe the operation of each device and circuit presented. Simulation results and results obtained from fabricated devices confirm the validity of these designs and approaches. Both the WP and WD circuits have a power consumption of approximately 2.4mW, during a weight update. If no weight update occurs the resting currents within the device are in the nA range, thus each circuit has a power consumption of approximately 1µW. A floating gate, FG, device fabricated using a standard CMOS process is presented. This device is to be integrated with both the WP and WD STDP circuits. The FG device is designed to store negative charge on a FG to represent the synaptic weight of the associated synapse. Charge is added or removed from the FG via Fowler-Nordheim tunnelling. This thesis outlines the design criteria and theoretical operation of this device. A model of the charge storage characteristics is presented and verified using HFCV and PCV experimental results. Limited precision weights, LPW, and its potential use in hardware neural networks is also considered. LPW offers a potential solution in the quest to design a compact FG device for use with CTS. The algorithms presented in this thesis show that LPW allows for a reduction in the synaptic weight storage device while permitting the network to function as intended

Self-healing concepts involving fine-grained redundancy for electronic systems

The start of the digital revolution came through the metal-oxide-semiconductor field-effect transistor (MOSFET) in 1959 followed by massive integration onto a silicon die by means of constant down scaling of individual components. Digital systems for certain applications require fault-tolerance against faults caused by temporary or permanent influence. The most widely used technique is triple module redundancy (TMR) in conjunction with a majority voter, which is regarded as a passive fault mitigation strategy. Design by functional resilience has been applied to circuit structures for increased fault-tolerance and towards self-diagnostic triggered self-healing. The focus of this thesis is therefore to develop new design strategies for fault detection and mitigation within transistor, gate and cell design levels. The research described in this thesis makes three contributions. The first contribution is based on adding fine-grained transistor level redundancy to logic gates in order to accomplish stuck-at fault-tolerance. The objective is to realise maximum fault-masking for a logic gate with minimal added redundant transistors. In the case of non-maskable stuck-at faults, the gate structure generates an intrinsic indication signal that is suitable for autonomous self-healing functions. As a result, logic circuitry utilising this design is now able to differentiate between gate faults and faults occurring in inter-gate connections. This distinction between fault-types can then be used for triggering selective self-healing responses. The second contribution is a logic matrix element which applies the three core redundancy concepts of spatial- temporal- and data-redundancy. This logic structure is composed of quad-modular redundant structures and is capable of selective fault-masking and localisation depending of fault-type at the cell level, which is referred to as a spatiotemporal quadded logic cell (QLC) structure. This QLC structure has the capability of cellular self-healing. Through the combination of fault-tolerant and masking logic features the QLC is designed with a fault-behaviour that is equal to existing quadded logic designs using only 33.3% of the equivalent transistor resources. The inherent self-diagnosing feature of QLC is capable of identifying individual faulty cells and can trigger self-healing features. The final contribution is focused on the conversion of finite state machines (FSM) into memory to achieve better state transition timing, minimal memory utilisation and fault protection compared to common FSM designs. A novel implementation based on content-addressable type memory (CAM) is used to achieve this. The FSM is further enhanced by creating the design out of logic gates of the first contribution by achieving stuck-at fault resilience. Applying cross-data parity checking, the FSM becomes equipped with single bit fault detection and correction