An embedded 1149.4 extension to support mixed-signal debugging

Debugging electronic circuits is traditionally done with bench equipment directly connected to the circuit under debug. In the digital domain, the difficulties associated with the direct physical access to circuit nodes led to the inclusion of resources providing support to that activity, first at the printed circuit level, and then at the integrated circuit level. The experience acquired with those solutions led to the emergence of dedicated infrastructures for debugging cores at the system-on-chip level. However, all these developments had a small impact in the analog and mixed-signal domain, where debugging still depends, to a large extent, on direct physical access to circuit nodes. As a consequence, when analog and mixed-signal circuits are integrated as cores inside a system-on-chip, the difficulties associated with debugging increase, which cause the time-to-market and the prototype verification costs to also increase. The present work considers the IEEE1149.4 infrastructure as a means to support the debugging of mixed-signal circuits, namely to access the circuit nodes and also an embedded debug mechanism named mixed-signal condition detector, necessary for watch-/breakpoints and real-time analysis operations. One of the main advantages associated with the proposed solution is the seamless migration to the system-on-chip level, as the access is done through electronic means, thus easing debugging operations at different hierarchical levels.

Development and plasticity of locomotor circuits in the zebrafish spinal cord

A fundamental goal in neurobiology is to understand the development and organization of neural circuits that drive behavior. In the embryonic spinal cord, the first motor activity is a slow coiling of the trunk that is sensory-independent and therefore appears to be centrally driven. Embryos later become responsive to sensory stimuli and eventually locomote, behaviors that are shaped by the integration of central patterns and sensory feedback. In this thesis I used a simple vertebrate model, the zebrafish, to investigate in three manners how developing spinal networks control these earliest locomotor behaviors. For the first part of this thesis, I characterized the rapid transition of the spinal cord from a purely electrical circuit to a hybrid network that relies on both chemical and electrical synapses. Using genetics, lesions and pharmacology we identified a transient embryonic behavior preceding swimming, termed double coiling. I used electrophysiology to reveal that spinal motoneurons had glutamate-dependent activity patterns that correlated with double coiling as did a population of descending ipsilateral glutamatergic interneurons that also innervated motoneurons at this time. This work (Knogler et al., Journal of Neuroscience, 2014) suggests that double coiling is a discrete step in the transition of the motor network from an electrically coupled circuit that can only produce simple coils to a spinal network driven by descending chemical neurotransmission that can generate more complex behaviors. In the second part of my thesis, I studied how spinal networks filter sensory information during self-generated movement. In the zebrafish embryo, mechanosensitive sensory neurons fire in response to light touch and excite downstream commissural glutamatergic interneurons to produce a flexion response, but spontaneous coiling does not trigger this reflex. I performed electrophysiological recordings to show that these interneurons received glycinergic inputs during spontaneous fictive coiling that prevented them from firing action potentials. Glycinergic inhibition specifically of these interneurons and not other spinal neurons was due to the expression of a unique glycine receptor subtype that enhanced the inhibitory current. This work (Knogler & Drapeau, Frontiers in Neural Circuits, 2014) suggests that glycinergic signaling onto sensory interneurons acts as a corollary discharge signal for reflex inhibition during movement. v In the final part of my thesis I describe work begun during my masters and completed during my doctoral degree studying how homeostatic plasticity is expressed in vivo at central synapses following chronic changes in network activity. I performed whole-cell recordings from spinal motoneurons to show that excitatory synaptic strength scaled up in response to decreased network activity, in accordance with previous in vitro studies. At the network level, I showed that homeostatic plasticity mechanisms were not necessary to maintain the timing of spinal circuits driving behavior, which appeared to be hardwired in the developing zebrafish. This study (Knogler et al., Journal of Neuroscience, 2010) provided for the first time important in vivo results showing that synaptic patterning is less plastic than synaptic strength during development in the intact animal. In conclusion, the findings presented in this thesis contribute widely to our understanding of the neural circuits underlying simple motor behaviors in the vertebrate spinal cord.

Large-Size AlGaN/GaN HEMT Large-Signal Electrothermal Characterization and Modeling for Wireless Digital Communications

The rapid growth in high data rate communication systems has introduced new high spectral efficient modulation techniques and standards such as LTE-A (long term evolution-advanced) for 4G (4th generation) systems. These techniques have provided a broader bandwidth but introduced high peak-to-average power ratio (PAR) problem at the high power amplifier (HPA) level of the communication system base transceiver station (BTS). To avoid spectral spreading due to high PAR, stringent requirement on linearity is needed which brings the HPA to operate at large back-off power at the expense of power efficiency. Consequently, high power devices are fundamental in HPAs for high linearity and efficiency. Recent development in wide bandgap power devices, in particular AlGaN/GaN HEMT, has offered higher power level with superior linearity-efficiency trade-off in microwaves communication. For cost-effective HPA design to production cycle, rigorous computer aided design (CAD) AlGaN/GaN HEMT models are essential to reflect real response with increasing power level and channel temperature. Therefore, large-size AlGaN/GaN HEMT large-signal electrothermal modeling procedure is proposed. The HEMT structure analysis, characterization, data processing, model extraction and model implementation phases have been covered in this thesis including trapping and self-heating dispersion accounting for nonlinear drain current collapse. The small-signal model is extracted using the 22-element modeling procedure developed in our department. The intrinsic large-signal model is deeply investigated in conjunction with linearity prediction. The accuracy of the nonlinear drain current has been enhanced through several issues such as trapping and self-heating characterization. Also, the HEMT structure thermal profile has been investigated and corresponding thermal resistance has been extracted through thermal simulation and chuck-controlled temperature pulsed I(V) and static DC measurements. Higher-order equivalent thermal model is extracted and implemented in the HEMT large-signal model to accurately estimate instantaneous channel temperature. Moreover, trapping and self-heating transients has been characterized through transient measurements. The obtained time constants are represented by equivalent sub-circuits and integrated in the nonlinear drain current implementation to account for complex communication signals dynamic prediction. The obtained verification of this table-based large-size large-signal electrothermal model implementation has illustrated high accuracy in terms of output power, gain, efficiency and nonlinearity prediction with respect to standard large-signal test signals.

Formal Multilevel Hierarchical Verification of Synchronous MOS Circuits

I have designed and implemented a system for the multilevel verification of synchronous MOS VLSI circuits. The system, called Silica Pithecus, accepts the schematic of an MOS circuit and a specification of the circuit's intended digital behavior. Silica Pithecus determines if the circuit meets its specification. If the circuit fails to meet its specification Silica Pithecus returns to the designer the reason for the failure. Unlike earlier verifiers which modelled primitives (e.g., transistors) as unidirectional digital devices, Silica Pithecus models primitives more realistically. Transistors are modelled as bidirectional devices of varying resistances, and nodes are modelled as capacitors. Silica Pithecus operates hierarchically, interactively, and incrementally. Major contributions of this research include a formal understanding of the relationship between different behavioral descriptions (e.g., signal, boolean, and arithmetic descriptions) of the same device, and a formalization of the relationship between the structure, behavior, and context of device. Given these formal structures my methods find sufficient conditions on the inputs of circuits which guarantee the correct operation of the circuit in the desired descriptive domain. These methods are algorithmic and complete. They also handle complex phenomena such as races and charge sharing. Informal notions such as races and hazards are shown to be derivable from the correctness conditions used by my methods.

Regional response differences across the human amygdaloid complex during social conditioning

The amygdala is consistently implicated in biologically relevant learning tasks such as Pavlovian conditioning. In humans, the ability to identify individual faces based on the social outcomes they have predicted in the past constitutes a critical form of associative learning that can be likened to “social conditioning.” To capture such learning in a laboratory setting, participants learned about faces that predicted negative, positive, or neutral social outcomes. Participants reported liking or disliking the faces in accordance with their learned social value. During acquisition, we observed differential functional magnetic resonance imaging activation across the human amygdaloid complex consistent with previous lesion, electrophysiological, and functional neuroimaging data. A region of the medial ventral amygdala and a region of the dorsal amygdala/substantia innominata showed signal increases to both Negative and Positive faces, whereas a lateral ventral region displayed a linear representation of the valence of faces such that Negative > Positive > Neutral. This lateral ventral locus also differed from the dorsal and medial loci in that the magnitude of these responses was more resistant to habituation. These findings document a role for the human amygdala in social learning and reveal coarse regional dissociations in amygdala activity that are consistent with previous human and nonhuman animal data.

Low cost BIST techniques for linear and non-linear analog circuits

With the ever increasing demands for high complexity consumer electronic products, market pressures demand faster product development and lower cost. SoCbased design can provide the required design flexibility and speed by allowing the use of IP cores. However, testing costs in the SoC environment can reach a substantial percent of the total production cost. Analog testing costs may dominate the total test cost, as testing of analog circuits usually require functional verification of the circuit and special testing procedures. For RF analog circuits commonly used in wireless applications, testing is further complicated because of the high frequencies involved. In summary, reducing analog test cost is of major importance in the electronic industry today. BIST techniques for analog circuits, though potentially able to solve the analog test cost problem, have some limitations. Some techniques are circuit dependent, requiring reconfiguration of the circuit being tested, and are generally not usable in RF circuits. In the SoC environment, as processing and memory resources are available, they could be used in the test. However, the overhead for adding additional AD and DA converters may be too costly for most systems, and analog routing of signals may not be feasible and may introduce signal distortion. In this work a simple and low cost digitizer is used instead of an ADC in order to enable analog testing strategies to be implemented in a SoC environment. Thanks to the low analog area overhead of the converter, multiple analog test points can be observed and specific analog test strategies can be enabled. As the digitizer is always connected to the analog test point, it is not necessary to include muxes and switches that would degrade the signal path. For RF analog circuits, this is specially useful, as the circuit impedance is fixed and the influence of the digitizer can be accounted for in the design phase. Thanks to the simplicity of the converter, it is able to reach higher frequencies, and enables the implementation of low cost RF test strategies. The digitizer has been applied successfully in the testing of both low frequency and RF analog circuits. Also, as testing is based on frequency-domain characteristics, nonlinear characteristics like intermodulation products can also be evaluated. Specifically, practical results were obtained for prototyped base band filters and a 100MHz mixer. The application of the converter for noise figure evaluation was also addressed, and experimental results for low frequency amplifiers using conventional opamps were obtained. The proposed method is able to enhance the testability of current mixed-signal designs, being suitable for the SoC environment used in many industrial products nowadays.

A current-mode based Field-Programmable Analog Array for signal processing applications

This paper presents a new approach to develop Field Programmable Analog Arrays (FPAAs),(1) which avoids excessive number of programming elements in the signal path, thus enhancing the performance. The paper also introduces a novel FPAA architecture, devoid of the conventional switching and connection modules. The proposed FPAA is based on simple current mode sub-circuits. An uncompounded methodology has been employed for the programming of the Configurable Analog Cell (CAC). Current mode approach has enabled the operation of the FPAA presented here, over almost three decades of frequency range. We have demonstrated the feasibility of the FPAA by implementing some signal processing functions.

Noise reduction in speech signals using a TMS320C31 digital signal processor

This paper describes a speech enhancement system (SES) based on a TMS320C31 digital signal processor (DSP) for real-time application. The SES algorithm is based on a modified spectral subtraction method and a new speech activity detector (SAD) is used. The system presents a medium computational load and a sampling rate up to 18 kHz can be used. The goal is load and a sampling rate up to 18 kHz can be used. The goal is to use it to reduce noise in an analog telephone line.

Discrete wavelet transform signal analyzer

This paper addresses the problem of processing biological data, such as cardiac beats in the audio and ultrasonic range, and on calculating wavelet coefficients in real time, with the processor clock running at a frequency of present application-specified integrated circuits and field programmable gate array. The parallel filter architecture for discrete wavelet transform (DWT) has been improved, calculating the wavelet coefficients in real time with hardware reduced up to 60%. The new architecture, which also processes inverse DWT, is implemented with the Radix-2 or the Booth-Wallace constant multipliers. One integrated circuit signal analyzer in the ultrasonic range, including series memory register banks, is presented. © 2007 IEEE.

Models and methods for power monolithic microwave integrated circuits

Computer aided design of Monolithic Microwave Integrated Circuits (MMICs) depends critically on active device models that are accurate, computationally efficient, and easily extracted from measurements or device simulators. Empirical models of active electron devices, which are based on actual device measurements, do not provide a detailed description of the electron device physics. However they are numerically efficient and quite accurate. These characteristics make them very suitable for MMIC design in the framework of commercially available CAD tools. In the empirical model formulation it is very important to separate linear memory effects (parasitic effects) from the nonlinear effects (intrinsic effects). Thus an empirical active device model is generally described by an extrinsic linear part which accounts for the parasitic passive structures connecting the nonlinear intrinsic electron device to the external world. An important task circuit designers deal with is evaluating the ultimate potential of a device for specific applications. In fact once the technology has been selected, the designer would choose the best device for the particular application and the best device for the different blocks composing the overall MMIC. Thus in order to accurately reproducing the behaviour of different-in-size devices, good scalability properties of the model are necessarily required. Another important aspect of empirical modelling of electron devices is the mathematical (or equivalent circuit) description of the nonlinearities inherently associated with the intrinsic device. Once the model has been defined, the proper measurements for the characterization of the device are performed in order to identify the model. Hence, the correct measurement of the device nonlinear characteristics (in the device characterization phase) and their reconstruction (in the identification or even simulation phase) are two of the more important aspects of empirical modelling. This thesis presents an original contribution to nonlinear electron device empirical modelling treating the issues of model scalability and reconstruction of the device nonlinear characteristics. The scalability of an empirical model strictly depends on the scalability of the linear extrinsic parasitic network, which should possibly maintain the link between technological process parameters and the corresponding device electrical response. Since lumped parasitic networks, together with simple linear scaling rules, cannot provide accurate scalable models, either complicate technology-dependent scaling rules or computationally inefficient distributed models are available in literature. This thesis shows how the above mentioned problems can be avoided through the use of commercially available electromagnetic (EM) simulators. They enable the actual device geometry and material stratification, as well as losses in the dielectrics and electrodes, to be taken into account for any given device structure and size, providing an accurate description of the parasitic effects which occur in the device passive structure. It is shown how the electron device behaviour can be described as an equivalent two-port intrinsic nonlinear block connected to a linear distributed four-port passive parasitic network, which is identified by means of the EM simulation of the device layout, allowing for better frequency extrapolation and scalability properties than conventional empirical models. Concerning the issue of the reconstruction of the nonlinear electron device characteristics, a data approximation algorithm has been developed for the exploitation in the framework of empirical table look-up nonlinear models. Such an approach is based on the strong analogy between timedomain signal reconstruction from a set of samples and the continuous approximation of device nonlinear characteristics on the basis of a finite grid of measurements. According to this criterion, nonlinear empirical device modelling can be carried out by using, in the sampled voltage domain, typical methods of the time-domain sampling theory.

Spectrum shaping and its application: spread-spectrum clock generator and circuits for ultra wide band

Electromagnetic spectrum can be identified as a resource for the designer, as well as for the manufacturer, from two complementary points of view: first, because it is a good in great demand by many different kind of applications; second, because despite its scarce availability, it may be advantageous to use more spectrum than necessary. This is the case of Spread-Spectrum Systems, those systems in which the transmitted signal is spread over a wide frequency band, much wider, in fact, than the minimum bandwidth required to transmit the information being sent. Part I of this dissertation deals with Spread-Spectrum Clock Generators (SSCG) aiming at reducing Electro Magnetic Interference (EMI) of clock signals in integrated circuits (IC) design. In particular, the modulation of the clock and the consequent spreading of its spectrum are obtained through a random modulating signal outputted by a chaotic map, i.e. a discrete-time dynamical system showing chaotic behavior. The advantages offered by this kind of modulation are highlighted. Three different prototypes of chaos-based SSCG are presented in all their aspects: design, simulation, and post-fabrication measurements. The third one, operating at a frequency equal to 3GHz, aims at being applied to Serial ATA, standard de facto for fast data transmission to and from Hard Disk Drives. The most extreme example of spread-spectrum signalling is the emerging ultra-wideband (UWB) technology, which proposes the use of large sections of the radio spectrum at low amplitudes to transmit high-bandwidth digital data. In part II of the dissertation, two UWB applications are presented, both dealing with the advantages as well as with the challenges of a wide-band system, namely: a chaos-based sequence generation method for reducing Multiple Access Interference (MAI) in Direct Sequence UWB Wireless-Sensor-Networks (WSNs), and design and simulations of a Low-Noise Amplifier (LNA) for impulse radio UWB. This latter topic was studied during a study-abroad period in collaboration with Delft University of Technology, Delft, Netherlands.

Application of Signal Advance Technology to Electrophysiology

Medical instrumentation used in diagnosis and treatment relies on the accurate detection and processing of various physiological events and signals. While signal detection technology has improved greatly in recent years, there remain inherent delays in signal detection/ processing. These delays may have significant negative clinical consequences during various pathophysiological events. Reducing or eliminating such delays would increase the ability to provide successful early intervention in certain disorders thereby increasing the efficacy of treatment. In recent years, a physical phenomenon referred to as Negative Group Delay (NGD), demonstrated in simple electronic circuits, has been shown to temporally advance the detection of analog waveforms. Specifically, the output is temporally advanced relative to the input, as the time delay through the circuit is negative. The circuit output precedes the complete detection of the input signal. This process is referred to as signal advance (SA) detection. An SA circuit model incorporating NGD was designed, developed and tested. It imparts a constant temporal signal advance over a pre-specified spectral range in which the output is almost identical to the input signal (i.e., it has minimal distortion). Certain human patho-electrophysiological events are good candidates for the application of temporally-advanced waveform detection. SA technology has potential in early arrhythmia and epileptic seizure detection and intervention. Demonstrating reliable and consistent temporally advanced detection of electrophysiological waveforms may enable intervention with a pathological event (much) earlier than previously possible. SA detection could also be used to improve the performance of neural computer interfaces, neurotherapy applications, radiation therapy and imaging. In this study, the performance of a single-stage SA circuit model on a variety of constructed input signals, and human ECGs is investigated. The data obtained is used to quantify and characterize the temporal advances and circuit gain, as well as distortions in the output waveforms relative to their inputs. This project combines elements of physics, engineering, signal processing, statistics and electrophysiology. Its success has important consequences for the development of novel interventional methodologies in cardiology and neurophysiology as well as significant potential in a broader range of both biomedical and non-biomedical areas of application.

Cellular mechanisms of operant and classical conditioning

The ability to associate a predictive stimulus with a subsequent salient event (i.e., classical conditioning) and the ability to associate an expressed behavior with the consequences (i.e., operant conditioning) allow for a predictive understanding of a changing environment. Although they are operationally distinct, there has been considerable debate whether at some fundamental level classical and operant conditioning are mechanistically distinct or similar. Feeding behavior of Aplysia (i.e., biting) was chosen as the model system and was successfully conditioned with appetitive forms of both operant and classical conditioning. The neuronal circuitry responsible for feeding is well understood and is suitable for cellular analyses, thus providing for a mechanistic comparison between these two forms of associative learning. ^ Neuron B51 is part of the feeding circuitry of Aplysia and is critical for the expression of ingestive behaviors. B51 also is a locus of plasticity following both operant and classical conditioning. Both in vivo and in vitro operant conditioning increased the input resistance and the excitability of B51. No pairing-specific changes in the input resistance were observed following both in vivo and in vitro classical conditioning. However, classical conditioning decreased the excitability of B51. Thus, both operant and classical conditioning modified the threshold level for activation of neuron B51, but in opposite directions, revealing key differences in the cellular mechanisms underlying these two forms of associative learning. ^ Next, the cellular mechanisms underlying operant conditioning were investigated in more detail using a single-cell analogue. The single-cell analogue successfully recapitulated the previous in vivo and in vitro operant conditioning results by increasing the input resistance and the excitability of B51. Both PKA and PKC were necessary for operant conditioning. Dopamine appears to be the transmitter mediating the reinforcement signal in this form of conditioning. A D1 dopamine receptor antibody revealed that the D1receptor localizes to the axon hillock, which is also the region that gives the strongest response when iontophoresing dopamine. ^ The studies presented herein, thus, provide for a greater understanding of the mechanisms underlying both of these forms of associative learning and demonstrate that they likely operate through distinct cellular mechanisms. ^

Blood-oxygen-level-dependent (BOLD) signal changes in total cortex and subcortex, pain, reward, and vigilance regions during mechanical pressure pain after morphine administration

Morphine is the most common clinical choice in the management of severe pain. Although the molecular mechanisms of morphine have already been characterized, the cerebral circuits by which it attenuates the sensation of pain have not yet been studied in humans. The objective of this two-arm (morphine versus placebo), between-subjects study was to examine whether morphine affects pain via pain-related cortical circuits, but also via reward regions that relate to the motivational state, as well as prefrontal regions that relate to vigilance as a result of morphine's sedative effects. Cortical activity was measured by the blood-oxygen-level-dependent (BOLD) signal changes using functional magnetic resonance imaging (fMRI). ^ The novelty of this study is at three levels: (i) to develop a methodology that will assess the average BOLD signal across subjects for the pain, reward, and vigilance cortical systems; (ii) to examine whether the reward and/or sedative effects of morphine are contributing factors to cortical regions associated with the motivational state and vigilance; and (iii) to propose a neuroanatomical model related to the opioid-sensitive effects of reward and sedation as a function of cortical activity related to pain in an effort to assess future analgesics. ^ Consistent with our hypotheses, our findings showed that the decrease in total pain-related volume activated between the post- and the pre-treatment morphine group was about 78%, while the post-treatment placebo group displayed only a 5% decrease when compared to pre-treatment levels of activation. The volume increase in reward regions was 451% in the post-treatment compared to the pre-treatment morphine condition. Finally, the volumetric decrease in vigilance regions was 63% in the posttreatment compared to the pre-treatment morphine condition. ^ These findings imply that changes in the blood flow of the reward and vigilance regions may be contributing factors in producing the analgesic effect under morphine administration. Future studies need to replicate this study in a higher resolution fMRI environment and to assess the proposed neuroanatomical model in patient populations. The necessity of pain research is apparent, since pain cuts across different diseases especially chronic ones, and thus, is recognized as a vital public health developing area. ^