Ultra-low Voltage Digital Circuits and Extreme Temperature Electronics Design

Certain applications require digital electronics to operate under extreme conditions e.g., large swings in ambient temperature, very low supply voltage, high radiation. Such applications include sensor networks, wearable electronics, unmanned aerial vehicles, spacecraft, and energyharvesting systems. This dissertation splits into two projects that study digital electronics supplied by ultra-low voltages and build an electronic system for extreme temperatures. The first project introduces techniques that improve circuit reliability at deep subthreshold voltages as well as determine the minimum required supply voltage. These techniques address digital electronic design at several levels: the physical process, gate design, and system architecture. This dissertation analyzes a silicon-on-insulator process, Schmitt-trigger gate design, and asynchronous logic at supply voltages lower than 100 millivolts. The second project describes construction of a sensor digital controller for the lunar environment. Parts of the digital controller are an asynchronous 8031 microprocessor that is compatible with synchronous logic, memory with error detection and correction, and a robust network interface. The digitial sensor ASIC is fabricated on a silicon-germanium process and built with cells optimized for extreme temperatures

Self-aligned silicidation of surround gate vertical MOSFETs for low cost RF applications

We report for the first time a CMOS-compatible silicidation technology for surround-gate vertical MOSFETs. The technology uses a double spacer comprising a polysilicon spacer for the surround gate and a nitride spacer for silicidation and is successfully integrated with a Fillet Local OXidation (FILOX) process, which thereby delivers low overlap capacitance and high drive-current vertical devices. Silicided 80-nm vertical n-channel devices fabricated using 0.5-?m lithography are compared with nonsilicided devices. A source–drain (S/D) activation anneal of 30 s at 1100 ?C is shown to deliver a channel length of 80 nm, and the silicidation gives a 60% improvement in drive current in comparison with nonsilicided devices. The silicided devices exhibit a subthreshold slope (S) of 87 mV/dec and a drain-induced barrier lowering (DIBL) of 80 mV/V, compared with 86 mV/dec and 60 mV/V for nonsilicided devices. S-parameter measurements on the 80-nm vertical nMOS devices give an fT of 20 GHz, which is approximately two times higher than expected for comparable lateral MOSFETs fabricated using the same 0.5-?m lithography. Issues associated with silicidation down the pillar sidewall are investigated by reducing the activation anneal time to bring the silicided region closer to the p-n junction at the top of the pillar. In this situation, nonlinear transistor turn-on is observed in drain-on-top operation and dramatically degraded drive current in source-on-top operation. This behavior is interpreted using mixed-mode simulations, which show that a Schottky contact is formed around the perimeter of the pillar when the silicided contact penetrates too close to the top S/D junction down the side of the pillar

Accurate leakage current models for MOSFET nanoscale devices

This paper underlines a closed forms of MOSFET transistor’sleakage current mechanisms inthe sub 100nmparadigm.The incorporation of draininduced barrier lowering (DIBL), Gate Induced Drain Lowering (GIDL) and body effect (m) on the sub-threshold leakage (Isub) wasinvestigated in detail. The Band-To-Band Tunneling (IBTBT) due to the source and Drain PN reverse junction were also modeled witha close and accurate model using a rectangularapproximation method (RJA). The three types of gate leakage (IG) were also modeled and analyzed for parasitic (IGO), inversion channel (IGC), and gate substrate (IGB).In addition, the leakage resources due to the aggressive reduction in the oxide thickness

Standby power consumption estimation by interacting leakage current mechanisms in nanoscaled CMOS digital circuits

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Adaptive Neural Coding Dependent on the Time-Varying Statistics of the Somatic Input Current

It is generally assumed that nerve cells optimize their performance to reflect the statistics of their input. Electronic circuit analogs of neurons require similar methods of self-optimization for stable and autonomous operation. We here describe and demonstrate a biologically plausible adaptive algorithm that enables a neuron to adapt the current threshold and the slope (or gain) of its current-frequency relationship to match the mean (or dc offset) and variance (or dynamic range or contrast) of the time-varying somatic input current. The adaptation algorithm estimates the somatic current signal from the spike train by way of the intracellular somatic calcium concentration, thereby continuously adjusting the neuronś firing dynamics. This principle is shown to work in an analog VLSI-designed silicon neuron

Memristor MOS Content Addressable Memory (MCAM): Hybrid Architecture for Future High Performance Search Engines

Large-capacity Content Addressable Memory (CAM) is a key element in a wide variety of applications. The inevitable complexities of scaling MOS transistors introduce a major challenge in the realization of such systems. Convergence of disparate technologies, which are compatible with CMOS processing, may allow extension of Moore's Law for a few more years. This paper provides a new approach towards the design and modeling of Memristor (Memory resistor) based Content Addressable Memory (MCAM) using a combination of memristor MOS devices to form the core of a memory/compare logic cell that forms the building block of the CAM architecture. The non-volatile characteristic and the nanoscale geometry together with compatibility of the memristor with CMOS processing technology increases the packing density, provides for new approaches towards power management through disabling CAM blocks without loss of stored data, reduces power dissipation, and has scope for speed improvement as the technology matures.Comment: 10 pages, 11 figure

Characterizing the firing properties of an adaptive analog VLSI neuron

Ben Dayan Rubin D, Chicca E, Indiveri G. Characterizing the firing properties of an adaptive analog VLSI neuron. Biologically Inspired Approaches to Advanced Information Technology. 2004;3141:189-200.We describe the response properties of a compact, low power, analog circuit that implements a model of a leaky-Integrate & Fire (I&F) neuron, with spike-frequency adaptation, refractory period and voltage threshold modulation properties. We investigate the statistics of the circuit's output response by modulating its operating parameters, like refractory period and adaptation level and by changing the statistics of the input current. The results show a clear match with theoretical prediction and neurophysiological data in a given range of the parameter space. This analysis defines the chip's parameter working range and predicts its behavior in case of integration into large massively parallel very-large-scale-integration (VLSI) networks

Statistical analysis and design of subthreshold operation memories

This thesis presents novel methods based on a combination of well-known statistical techniques for faster estimation of memory yield and their application in the design of energy-efficient subthreshold memories. The emergence of size-constrained Internet-of-Things (IoT) devices and proliferation of the wearable market has brought forward the challenge of achieving the maximum energy efficiency per operation in these battery operated devices. Achieving this sought-after minimum energy operation is possible under sub-threshold operation of the circuit. However, reliable memory operation is currently unattainable at these ultra-low operating voltages because of the memory circuit's vanishing noise margins which shrink further in the presence of random process variations. The statistical methods, presented in this thesis, make the yield optimization of the sub-threshold memories computationally feasible by reducing the SPICE simulation overhead. We present novel modifications to statistical sampling techniques that reduce the SPICE simulation overhead in estimating memory failure probability. These sampling scheme provides 40x reduction in finding most probable failure point and 10x reduction in estimating failure probability using the SPICE simulations compared to the existing proposals. We then provide a novel method to create surrogate models of the memory margins with better extrapolation capability than the traditional regression methods. These models, based on Gaussian process regression, encode the sensitivity of the memory margins with respect to each individual threshold variation source in a one-dimensional kernel. We find that our proposed additive kernel based models have 32% smaller out-of-sample error (that is, better extrapolation capability outside training set) than using the six-dimensional universal kernel like Radial Basis Function (RBF). The thesis also explores the topological modifications to the SRAM bitcell to achieve faster read operation at the sub-threshold operating voltages. We present a ten-transistor SRAM bitcell that achieves 2x faster read operation than the existing ten-transistor sub-threshold SRAM bitcells, while ensuring similar noise margins. The SRAM bitcell provides 70% reduction in dynamic energy at the cost of 42% increase in the leakage energy per read operation. Finally, we investigate the energy efficiency of the eDRAM gain-cells as an alternative to the SRAM bitcells in the size-constrained IoT devices. We find that reducing their write path leakage current is the only way to reduce the read energy at Minimum Energy operation Point (MEP). Further, we study the effect of transistor up-sizing under the presence of threshold voltage variations on the mean MEP read energy by performing statistical analysis based on the ANOVA test of the full-factorial experimental design.Esta tesis presenta nuevos métodos basados en una combinación de técnicas estadísticas conocidas para la estimación rápida del rendimiento de la memoria y su aplicación en el diseño de memorias de energia eficiente de sub-umbral. La aparición de los dispositivos para el Internet de las cosas (IOT) y la proliferación del mercado portátil ha presentado el reto de lograr la máxima eficiencia energética por operación de estos dispositivos operados con baterias. La eficiencia de energía es posible si se considera la operacion por debajo del umbral de los circuitos. Sin embargo, la operación confiable de memoria es actualmente inalcanzable en estos bajos niveles de voltaje debido a márgenes de ruido de fuga del circuito de memoria, los cuales se pueden reducir aún más en presencia de variaciones randomicas de procesos. Los métodos estadísticos, que se presentan en esta tesis, hacen que la optimización del rendimiento de las memorias por debajo del umbral computacionalmente factible mediante la simulación SPICE. Presentamos nuevas modificaciones a las técnicas de muestreo estadístico que reducen la sobrecarga de simulación SPICE en la estimación de la probabilidad de fallo de memoria. Estos esquemas de muestreo proporciona una reducción de 40 veces en la búsqueda de puntos de fallo más probable, y 10 veces la reducción en la estimación de la probabilidad de fallo mediante las simulaciones SPICE en comparación con otras propuestas existentes. A continuación, se proporciona un método novedoso para crear modelos sustitutos de los márgenes de memoria con una mejor capacidad de extrapolación que los métodos tradicionales de regresión. Estos modelos, basados en el proceso de regresión Gaussiano, codifican la sensibilidad de los márgenes de memoria con respecto a cada fuente de variación de umbral individual en un núcleo de una sola dimensión. Los modelos propuestos, basados en kernel aditivos, tienen un error 32% menor que el error out-of-sample (es decir, mejor capacidad de extrapolación fuera del conjunto de entrenamiento) en comparacion con el núcleo universal de seis dimensiones como la función de base radial (RBF). La tesis también explora las modificaciones topológicas a la celda binaria SRAM para alcanzar velocidades de lectura mas rapidas dentro en el contexto de operaciones en el umbral de tensiones de funcionamiento. Presentamos una celda binaria SRAM de diez transistores que consigue aumentar en 2 veces la operación de lectura en comparacion con las celdas sub-umbral de SRAM de diez transistores existentes, garantizando al mismo tiempo los márgenes de ruido similares. La celda binaria SRAM proporciona una reducción del 70% en energía dinámica a costa del aumento del 42% en la energía de fuga por las operaciones de lectura. Por último, se investiga la eficiencia energética de las células de ganancia eDRAM como una alternativa a los bitcells SRAM en los dispositivos de tamaño limitado IOT. Encontramos que la reducción de la corriente de fuga en el path de escritura es la única manera de reducir la energía de lectura en el Punto Mínimo de Energía (MEP). Además, se estudia el efecto del transistor de dimensionamiento en virtud de la presencia de variaciones de voltaje de umbral en la media de energia de lecture MEP mediante el análisis estadístico basado en la prueba de ANOVA del diseño experimental factorial completo.Postprint (published version