CMOS Hyperbolic Sine ELIN filters for low/audio frequency biomedical applications

Hyperbolic-Sine (Sinh) filters form a subclass of Externally-Linear-Internally-Non- Linear (ELIN) systems. They can handle large-signals in a low power environment under half the capacitor area required by the more popular ELIN Log-domain filters. Their inherent class-AB nature stems from the odd property of the sinh function at the heart of their companding operation. Despite this early realisation, the Sinh filtering paradigm has not attracted the interest it deserves to date probably due to its mathematical and circuit-level complexity. This Thesis presents an overview of the CMOS weak inversion Sinh filtering paradigm and explains how biomedical systems of low- to audio-frequency range could benefit from it. Its dual scope is to: consolidate the theory behind the synthesis and design of high order Sinh continuous–time filters and more importantly to confirm their micro-power consumption and 100+ dB of DR through measured results presented for the first time. Novel high order Sinh topologies are designed by means of a systematic mathematical framework introduced. They employ a recently proposed CMOS Sinh integrator comprising only p-type devices in its translinear loops. The performance of the high order topologies is evaluated both solely and in comparison with their Log domain counterparts. A 5th order Sinh Chebyshev low pass filter is compared head-to-head with a corresponding and also novel Log domain class-AB topology, confirming that Sinh filters constitute a solution of equally high DR (100+ dB) with half the capacitor area at the expense of higher complexity and power consumption. The theoretical findings are validated by means of measured results from an 8th order notch filter for 50/60Hz noise fabricated in a 0.35μm CMOS technology. Measured results confirm a DR of 102dB, a moderate SNR of ~60dB and 74μW power consumption from 2V power supply

Methods for synthesis of multiple-input translinear element networks

Translinear circuits are circuits in which the exponential relationship between the output current and input voltage of a circuit element is exploited to realize various algebraic or differential equations. This thesis is concerned with a subclass of translinear circuits, in which the basic translinear element, called a multiple-input translinear element (MITE), has an output current that is exponentially related to a weighted sum of its input voltages. MITE networks can be used for the implementation of the same class of functions as traditional translinear circuits. The implementation of algebraic or (algebraic) differential equations using MITEs can be reduced to the implementation of the product-of-power-law (POPL) relationships, in which an output is given by the product of inputs raised to different powers. Hence, the synthesis of POPL relationships, and their optimization with respect to the relevant cost functions, is very important in the theory of MITE networks. In this thesis, different constraints on the topology of POPL networks that result in desirable system behavior are explored and different methods of synthesis, subject to these constraints, are developed. The constraints are usually conditions on certain matrices of the network, which characterize the weights in the relevant MITEs. Some of these constraints are related to the uniqueness of the operating point of the network and the stability of the network. Conditions that satisfy these constraints are developed in this work. The cost functions to be minimized are the number of MITEs and the number of input gates in each MITE. A complete solution to POPL network synthesis is presented here that minimizes the number of MITEs first and then minimizes the number of input gates to each MITE. A procedure for synthesizing POPL relationships optimally when the number of gates is minimal, i.e., 2, has also been developed here for the single--output case. A MITE structure that produces the maximum number of functions with minimal reconfigurability is developed for use in MITE field--programmable analog arrays. The extension of these constraints to the synthesis of linear filters is also explored, the constraint here being that the filter network should have a unique operating point in the presence of nonidealities. Synthesis examples presented here include nonlinear functions like the arctangent and the gaussian function which find application in analog implementations of particle filters. Synthesis of dynamical systems is presented here using the examples of a Lorenz system and a sinusoidal oscillator. The procedures developed here provide a structured way to automate the synthesis of nonlinear algebraic functions and differential equations using MITEs.Ph.D.Committee Chair: Anderson, David; Committee Member: Habetler, Thomas; Committee Member: Hasler, Paul; Committee Member: McClellan, James; Committee Member: Minch, Bradle

Application of Nonlinear Transistor Characteristics

This research presents three works all related by the subject of third-order distortion reduction in nonlinear circuits. Each one is a novel extension to previous work in that branch of electronics literature. All three follow the procedure of presenting a novel algebraic proof and following up with simulations and/or measurements to confirm the theoretical result. The works are generally themed around nonlinear low-frequency bipolar transistor circuits. Firstly, an investigation is conducted into a well documented effect in bipolar-junction transistors (BJTs) called inherent third-order distortion nulling. This effect is shown to be a fundamental result of the transistor’s transfer junction acting upon an input signal. The proof of a single BJT emitter-follower amplifier’s inherent null is examined which is well documented in the literature. This forms the basis for a novel extension in Darlington transistors where theory suggests the third-order null occurs at double the collector current of a single BJT. Discrete measurements of a CA3083 transistor array are undertaken and compared with theory and simulation data. These measurements confirm theory with reasonable accuracy. A temperature and process variation independent bias circuit is developed to solve one issue with using third-order distortion nulling. This work is interesting in that it branches into series resistance compensation for translinear circuits and stands as a useful circuit in its own right. Using stacks of matched forward-biased semiconductor junctions which conform to translinear conditions, a bias current can be generated which theoretically removes temperature and series resistance dependence on the particular BJT used. This proves useful for the previous work in distortion nulling, but also allows direct and accurate measurement of series resistance. Again, simulation and measurement data is obtained from discrete measurements of the proposed circuit, and the results conform with theory to a reasonable degree. Lastly, this work presents the analysis of a cascoded-compensation (Cascomp) amplifier. It presents the first fully nonlinear derivation of the Cascomp’s transfer function and its associated harmonic and intermodulation distortion components. The derivation reveals an interesting characteristic in which the third-order intermodulation distortion has multiple local minima. This characteristic has not yet been presented in the literature, and allows better optimisation of Cascomp amplifiers in any application. Again, this characteristic and its potential benefits are confirmed with simulation and discrete measurements. Observations of the presented works are discussed and built upon in the last chapter. This leads to suggestions on future research topics branching on from these works

Synthesis and analysis of nonlinear, analog, ultra low power, Bernoulli cell based CytoMimetic circuits for biocomputation

A novel class of analog BioElectronics is introduced for the systematic implementation of ultra-low power microelectronic circuits, able to compute nonlinear biological dynamics. This class of circuits is termed ``CytoMimetic Circuits'', in an attempt to highlight their actual function, which is mimicking biological responses, as observed experimentally. Inspired by the ingenious Bernoulli Cell Formalism (BCF), which was originally formulated for the modular synthesis and analysis of linear, time-invariant, high-dynamic range, logarithmic filters, a new, modified mathematical framework has been conceived, termed Nonlinear Bernoulli Cell Formalism (NBCF), which forms the core mathematical framework, characterising the operation of CytoMimetic circuits. The proposed nonlinear, transistor-level mathematical formulation exploits the striking similarities existing between the NBCF and coupled ordinary differential equations, typically appearing in models of naturally encountered biochemical systems. The resulting continuous-time, continuous-value, low-power CytoMimetic electronic circuits succeed in simulating with good accuracy cellular and molecular dynamics and found to be in very good agreement with their biological counterparts. They usually occupy an area of a fraction of a square millimetre, while consuming between hundreds of nanowatts and few tenths of microwatts of power. The systematic nature of the NBCF led to the transformation of a wide variety of biochemical reactions into nonlinear Log-domain circuits, which span a large area of different biological model types. Moreover, a detailed analysis of the robustness and performance of the proposed circuit class is also included in this thesis. The robustness examination has been conducted via post-layout simulations of an indicative CytoMimetic circuit and also by providing fabrication-related variability simulations, obtained by means of analog Monte Carlo statistical analysis for each one of the proposed circuit topologies. Furthermore, a detailed mathematical analysis that is carefully addressing the effect of process-parameters and MOSFET geometric properties upon subthreshold translinear circuits has been conducted for the fundamental translinear blocks, CytoMimetic topologies are comprised of. Finally, an interesting sub-category of Neuromorphic circuits, the ``Log-Domain Silicon Synapses'' is presented and representative circuits are thoroughly analysed by a novel, generalised BC operator framework. This leads to the conclusion that the BC operator consists the heart of such Log-domain circuits, therefore, allows the establishment of a general class of BC-based silicon synaptic circuits, which includes most of the synaptic circuits, implemented so far in Log-domain.Open Acces

A 0.8V, 7μA, rail-to-rail input/output, constant Gm operational amplifier in standard digital 0.18μm CMOS

A two-stage amplifier, operational at 0.8V and drawing 7/spl mu/A, has been integrated in a standard digital 0.18/spl mu/m CMOS process. Rail-to-rail operations at the input are enabled by complementary transistor pairs with g/sub m/ control. The efficient rail-to-rail output stage is biased in class AB. The measured DC gain of the amplifier is 75dB, and the unity-gain frequency is 870kHz with a 12pF, 100k/spl Omega/load. Both input and output stage transistors are biased in weak inversion

The stochastic neural network in VLSI for studying noise communication in crayfish

L'attivita neurale in natura presenta un andamento stocastico e gioca un ruolo significativo nel cervello. Tuttavia, la maggior parte degli articoli si limitano alla simulazione di neuroni stocastici. In questa tesi, proponiamo un nuovo modello stocastico secondo il formalismo di Hodgkin-Huxley basato su equazioni dierenziali stocastiche e moto browniano. Il nuovo modello di equazione dierenziale stocastiche riproduce una vasta gamma di dinamiche in modo piu realistico rispetto ai precedenti modelli deterministici. Tale modello stocastico e stato applicata a una semplice rete neurale che si trova sulla coda di un gambero chiamato CPR (caudal photoreceptor). Presentiamo una libreria di operatori analogici stocastici utilizzati per il calcolo analogico in tempo reale. Questa libreria permette di ottenere una implementazione in silicio della rete stocastica CPR che sarà collegata alle cellule nervose del gambero. L'interazione vivente-articiali permettera ai biologisti di comprendere meglio i fenomeni nervosi // The Neural activity in nature presents a stochastic trend and plays an important role in the brain. However, most papers are limited simulating stochastic neurons. In this thesis, we propose a novel stochastic model according to the Hodgkin{Huxley formalism using stochastic dierential equations and Brownian motion. The new stochastic dierential equation model reproduces a large range of dynamics more realistically than previous deterministic models. Such stochastic model has been applied to simple neural network that is located on the tail of the craysh called CPR (caudal photoreceptor). We present a library of stochastic analog operators used for the analog real-time computation. This library allows to obtain a silicon implementation of the CPR stochastic network that will be connected to the nerve cells of the craysh. The living-articial interaction will allow biologists to better understand the nervous phenomen

Analysis of Current Conveyor based Switched Capacitor Circuits for Application in ∆Σ Modulators

The reduction in supply voltage, loss of dynamic range and increased noise prevent the analog circuits from taking advantage of advanced technologies. Therefore the trend is to move all signal processing tasks to digital domain where advantages of technology scaling can be used. Due to this, there exists a need for data converters with large signal bandwidths, higher speeds and greater dynamic range to act as an interface between real world analog and digital signals. The Delta Sigma (∆Σ) modulator is a data converter that makes use of large sampling rates and noise shaping techniques to achieve high resolution in the band of interest. The modulator consists of analog integrators and comparators which create a modulated digital bit stream whose average represents the input value. Due to their simplicity, they are popular in narrow band receivers, medical and sensor applications. However Operational Amplifiers (Op-Amps) or Operational Transconductance Amplifiers (OTAs), which are commonly used in data converters, present a bottleneck. Due to low supply voltages, designers rely on folded cascode, multistage cascade and bulk driven topologies for their designs. Although the two stage or multistage cascade topologies offer good gain and bandwidth, they suffer from stability problems due to multiple stages and feedback requiring large compensation capacitors. Therefore other low voltage Switched-Capacitor (SC) circuit techniques were developed to overcome these problems, based on inverters, comparators and unity gain buffers. In this thesis we present an alternative approach to design of ∆Σ modulators using Second Generation Current Conveyors (CCIIs). The important feature of these modulators is the replacement of the traditional Op-Amp based SC integrators with CCII based SC integrators. The main design issues such as the effect of the non-idealities in the CCIIs are considered in the operation of SC circuits and solutions are proposed to cancel them. Design tradeoffs and guidelines for various components of the circuit are presented through analysis of existing and the proposed SC circuits. A two step adaptive calibration technique is presented which uses few additional components to measure the integrator input output characteristic and linearize it for providing optimum performance over a wide range of sampling frequencies while maintaining low power and area. The presented CCII integrator and calibration circuit are used in the design of a 4th order (2-2 cascade) ∆Σ modulator which has been fabricated in UMC 90nm/1V technology through Europractice. Experimental values for Signal to Noise+Distortion Ratio (SNDR), Dynamic Range (DR) and Figure Of Merit (FOM) show that the modulator can compete with state of art reconfigurable Discrete-Time (DT) architectures while using lower gain stages and less design complexity

Design of Neuromemristive Systems for Visual Information Processing

Neuromemristive systems (NMSs) are brain-inspired, adaptive computer architectures based on emerging resistive memory technology (memristors). NMSs adopt a mixed-signal design approach with closely-coupled memory and processing, resulting in high area and energy efficiencies. Previous work suggests that NMSs could even supplant conventional architectures in niche application domains such as visual information processing. However, given the infancy of the field, there are still several obstacles impeding the transition of these systems from theory to practice. This dissertation advances the state of NMS research by addressing open design problems spanning circuit, architecture, and system levels. Novel synapse, neuron, and plasticity circuits are designed to reduce NMSs’ area and power consumption by using current-mode design techniques and exploiting device variability. Circuits are designed in a 45 nm CMOS process with memristor models based on multilevel (W/Ag-chalcogenide/W) and bistable (Ag/GeS2/W) device data. Higher-level behavioral, power, area, and variability models are ported into MATLAB to accelerate the overall simulation time. The circuits designed in this work are integrated into neural network architectures for visual information processing tasks, including feature detection, clustering, and classification. Networks in the NMSs are trained with novel stochastic learning algorithms that achieve 3.5 reduction in circuit area, reduced design complexity, and exhibit similar convergence properties compared to the least-mean-squares algorithm. This work also examines the effects of device-level variations on NMS performance, which has received limited attention in previous work. The impact of device variations is reduced with a partial on-chip training methodology that enables NMSs to be configured with relatively sophisticated algorithms (e.g. resilient backpropagation), while maximizing their area-accuracy tradeoff