Proceedings of the 18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems, which took place in Dresden, Germany, 26 – 28 May 2010.

<p>Implantable wireless devices are used to treat a variety of diseases that are not able to be treated with pharmaceuticals or traditional surgery, These implantable devices have use in the treatment of neurological disorders like epilepsy, optical disorders such as glaucoma, or injury related issues such as targeted muscle reinnervation. These devices can rely upon harvesting power from an inductive wireless power source and batteries. Improvements to how well the devices utilize this power directly increase the efficacy of the device operation as well as the device's lifetime, reducing the need for future surgeries or implantations. </p> <p> I have designed an improvement to cavity resonator based wireless power by designing a dynamic impedance matching implantable power supply, capable of tracking with device motion throughout a changing magnetic field and tracking with changing powering frequencies. This cavity resonator based system presents further challenges practically in the turn-on cycle of the improved device. </p> <p> I further design a coil-to-coil based wireless power system, capable of dynamically impedance matching a high quality factor coil to optimize power transfer during steady state, while also reducing turn-on transient power required in dynamic systems by utilizing a second low quality factor coil. This second coil has a broadband response and is capable of turning on at lower powers than that of the high quality factor coil. The low quality factor coil powers the circuitry that dynamically matches the impedance of the high quality factor coil, allowing for low power turn on while maintaining high power transfer at all operating frequencies to the implantable device. </p> <p> Finally, an integrated circuit is designed, fabricated, and tested that is capable of smoothly providing regulated DC power to the implantable device by stepping up from wireless power to a reasonable voltage level or stepping down from a battery to a reasonable voltage level for the device. The chip is fabricated in 0.18um CMOS process and is capable of providing power to the "Bionode" implantable device. </p>

The field of coupled resonant systems is a rich research area with enumerable real-world applications, including the fields of neural computing and pattern recognition, energy harvesting, and even modeling the behavior of certain types of biological systems. This work is primarily focused on the study of the behaviors of two subsets of this field: large networks of globally coupled resonators (which, in this work, refers to passive, damped resonant elements which require external stimulus) and smaller networks of oscillators (referring to active devices capable of self-sustained motion), which are coupled through a network of light-sensitive resistive elements. In the case of the former, we begin by developing an analytical and experimental framework to examine the behaviors of this system under various conditions, such as different coupling modalities and element-level parametric mistunings. Once a proper understanding of the dynamics of these systems has been established, we go on to develop the system into a single-input, single-output, multi-analyte volatile organic compound sensor. For the study of oscillator networks, we begin by building a device which utilizes a network of Colpitts oscillators, coupled through a series of color-filtered CdSe photocells. We then establish that through the analysis of particular emergent behaviors (most notably, frequency locking within the network), this type of system may show promise as a threshold color sensor. By exploiting these behaviors, this type of system may find applications in neuromorphic computing (particularly in optical pattern recognition).

Proceedings of the 18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems, which took place in Dresden, Germany, 26 – 28 May 2010.:Welcome Address ........................ Page I Table of Contents ........................ Page III Symposium Committees .............. Page IV Special Thanks ............................. Page V Conference program (incl. page numbers of papers) ................... Page VI Conference papers Invited talks ................................ Page 1 Regular Papers ........................... Page 14 Wednesday, May 26th, 2010 ......... Page 15 Thursday, May 27th, 2010 .......... Page 110 Friday, May 28th, 2010 ............... Page 210 Author index ............................... Page XIII

<p>  </p> <p>Many machine learning models exist for battery management systems to utilize. Few have been shown to work. This work focuses on gathering data from cycling battery packs and sending this data directly to machine learning models built off robust datasets for applying the resulting predicted values and outputs directly on top of real-time systems. A parasitic sensor network was created composed of a main microcontroller, a host CPU, and various sensors including resistance temperature detection devices (RTDs), a voltage measurement circuit, current measurement circuit, and an accelerometer/gyroscope. The resulting network was integrated parasitically with a 4-cell 18650 SONY VTC6 battery pack, then tested both on-ground and in-flight with a commercial quadcopter. Real-time data for the battery pack with four cells in series was gathered. This real-time data stream was then integrated with data-driven neural network algorithms trained on various 18650 datasets and a real physical model to finalize the “AI BMS”. Using the power of non-linear models to infer battery health impacts not normally considered in battery management systems, the “AI BMS” was able to use low-fidelity real-time data in conjunction with a powerful multi-faceted model to make predictive decisions about battery health characteristics on top of normal system operations.</p>

<p>With the advent of increasingly complex radio systems at higher frequencies and the slowing of traditional CMOS process scaling with power concerns, there has been an increased focus on integration, architectural, and material innovations as a continued path forward in MEMS and logic. This work presents the first comprehensive experimental study of resonant body transistors in a commercial 14nm FinFET process, demonstrating differential radio frequency transduction as a function of transistor biasing through electrostatic, piezoresistive, and threshold voltage modulation. The impact of device design changes on unreleased resonator performance are further explored, highlighting the importance of phononic confinement in achieving an f*Q product of 8.2*10¹¹ at 11.73 GHz. Also shown are initial efforts towards the understanding of coupled oscillator architectures and a perovskite nickelate material system. Finally, development of resonators based on two-dimensional materials, whose scale is particularly attractive for high-frequency nano-mechanical resonators and acoustic devices, is discussed. Experiments towards dry transfer of tellurene flakes using geometries printed via two photon polymerization are presented along with optimization of a fabrication process for gated RF devices, presenting new opportunities for high-frequency electro-mechanical interactions in this topological material. </p>

<p dir="ltr">Interconnects pose severe performance bottlenecks in advanced technology nodes due to multiple scaling challenges. To understand such problems and explore potential solutions, it is important to develop advanced models. This is particularly relevant for modern interconnects (especially vias) with complex structures with non- trivial current paths. In this dissertation, we develop a comprehensive physics-based interconnect models to capture surface and grain boundary scattering. We further analyze the circuit implications of 2D transition metal dichalcogenide (TMD)-augmented interconnects, which show potential in mitigating some of the scalability concerns of state-of-the-art interconnects. First, we propose a 2D spatially resolved model for surface scattering in rectangular interconnects based on the Fuchs-Sondheimer (FS) theory. The proposed spatially resolved FS (SRFS) model offers both spatial dependence and explicit relation of conductivity to physical parameters. We also couple the SRFS model with grain boundary scattering based on the Mayadas−Shatzkes (MS) theory. The SRFS-MS model is exact for diffusive surface scattering and offers a good approximation for elastic surface scattering. Furthermore, we develop a circuit-compatible version of the SRFS-MS model and show a close match with the physical SRFS-MS model (error < 0.7%). Moreover, we integrate temperature dependency, confirming that surface scattering has a negligible temperature-dependence. Second, we analyze the circuit implications of 2D TMD augmented interconnects and show the effective clock frequency of an AES circuit is boosted by 2%-32%. We also establish that the vertical resistivity of the TMD material must be below 22 kΩ-nm to obtain performance benefits over state-of-the-art interconnects in the worst-case process-temperature corner.</p>

For ultra-high speed communication systems at high center frequencies above 100 GHz, we propose a disruptive change in system architecture to address major issues regarding amplifier chains with a large number of amplifier stages. They cause a high noise figure and high power consumption when operating close to the frequency limits of the underlying semiconductor technologies. Instead of scaling a classic homodyne transceiver system, we employ repeated amplification in single-stage amplifiers through positive feedback as well as synthesizer-free self-mixing demodulation at the receiver to simplify the system architecture notably. Since the amplitude and phase information for the emerging oscillation is defined by the input signal and the oscillator is only turned on for a very short time, it can be left unstabilized and thus come without a PLL. As soon as gain is no longer the most prominent issue, relaxed requirements for all the other major components allow reconsidering their implementation concepts to achieve further improvements compared to classic systems. This paper provides the first comprehensive overview of all major design aspects that need to be addressed upon realizing a SPARS-based transceiver. At system level, we show how to achieve high data rates and a noise performance comparable to classic systems, backed by scaled demonstrator experiments. Regarding the transmitter, design considerations for efficient quadrature modulation are discussed. For the frontend components that replace PA and LNA amplifier chains, implementation techniques for regenerative sampling circuits based on super-regenerative oscillators are presented. Finally, an analog-to-digital converter with outstanding performance and complete interfaces both to the analog baseband as well as to the digital side completes the set of building blocks for efficient ultra-high speed communication.

<p>Neuromorphic computing endeavors to develop computational systems capable of emulating the brain’s capacity to execute intricate tasks concurrently and with remarkable energy efficiency. By utilizing new bioinspired computing architectures, these systems have the potential to revolutionize high-performance computing and enable local, low-energy computing for sensors and robots. Organic and soft materials are particularly attractive for neuromorphic computing as they offer biocompatibility, low-energy switching, and excellent tunability at a relatively low cost. Additionally, organic materials provide physical flexibility, large-area fabrication, and printability.</p><p>This doctoral dissertation showcases the research conducted in fabricating a comprehensive spiking organic neuron, which serves as the fundamental constituent of a circuit system for neuromorphic computing. The major contribution of this dissertation is the development of the organic, flexible neuron composed of spiking synapses and somas utilizing ultra-low voltage organic field-effect transistors (OFETs) for information processing. The synaptic and somatic circuits are implemented using physically flexible and biocompatible organic electronics necessary to realize the Polymer Neuromorphic Circuitry. An Axon-Hillock (AH) somatic circuit was fabricated and analyzed, followed by the adaptation of a log-domain integrator (LDI) synaptic circuit and the fabrication and analysis of a differential-pair integrator (DPI). Finally, a spiking organic neuron was formed by combining two LDI synaptic circuits and one AH synaptic circuit, and its characteristics were thoroughly examined. This is the first demonstration of the fabrication of an entire neuron using solid-state organic materials over a flexible substrate with integrated complementary OFETs and capacitors.</p>

<p dir="ltr">This project aimed to develop an instrumentation and control system for a micro-drilling robot based on Industrial Internet of Things (IIoT) technologies. The automation system integrated IIoT technological tools to create a robust automation system capable of being used in drilling operations. The system incorporated industrial-grade sensors, which carried out direct measurements of the critical variables of the process. The indirect variables relevant to the control of the robot were calculated from the measured parameters. The system also considered the telemetry architecture necessary to reliably transmit data from the down-the-hole (DTH) robot to a receiver on the surface. Telemetry was based on wireless communication through long-range radio frequency (LoRa). The system developed had models based on Artificial Intelligence (AI) and Machine Learning (ML) for determining the mode of operation, detecting changes in the process, and changes in drilling variables in critical hydraulic components for the drilling process. Algorithms based on AI and ML models also allowed the user to make better decisions based on the variables' correlation to optimize the drilling process (e.g., dynamic change of flow, pressure, and RPMs based on automatic rock identification). A user interface (UI) was developed, and digital tools to perform data analysis were implemented. Safety assessment in all robot systems (e.g., electrical, hardware, software) was contemplated as a critical design component. The result of this research project provides innovative micro-drilling robots with the necessary technological tools to optimize the drilling process. The system made drilling more efficient, reliable, and safe, providing diagnostic and prognostic tools that allowed planning maintenance based on the actual health of the devices. The system that was developed was tested in a test bench under controlled conditions within a laboratory to characterize the system and collect data that allowed ML models' development, training, validation, and testing. The prototype of a micro-drilling robot installed on the test bench served as a case study to assess the implemented models' reliability and the proposed telemetry.</p>