Analog Signal Buffering and Reconstruction

Wireless sensor networks (WSNs) are capable of a myriad of tasks, from monitoring critical infrastructure such as bridges to monitoring a person\u27s vital signs in biomedical applications. However, their deployment is impractical for many applications due to their limited power budget. Sleep states are one method used to conserve power in resource-constrained systems, but they necessitate a wake-up circuit for detecting unpredictable events. In conventional wake-up-based systems, all information preceding a wake-up event will be forfeited. To avoid this data loss, it is necessary to include a buffer that can record prelude information without sacrificing the power savings garnered by the active use of sleep states.;Unfortunately, traditional memory buffer systems utilize digital electronics which are costly in terms of power. Instead of operating in the target signal\u27s native analog environment, a digital buffer must first expend a great deal of energy to convert the signal into a digital signal. This issue is further compounded by the use of traditional Nyquist sampling which does not adapt to the characteristics of a dynamically changing signal. These characteristics reveal why a digital buffer is not an appropriate choice for a WSN or other resource-constrained system.;This thesis documents the development of an analog pre-processing block that buffers an incoming signal using a new method of sampling. This method requires sampling only local maxima and minima (both amplitude and time), effectively approximating the instantaneous Nyquist rate throughout a time-varying signal. The use of this sampling method along with ultra-low-power analog electronics enables the entire system to operate in the muW power levels. In addition to these power saving techniques, a reconfigurable architecture will be explored as infrastructure for this system. This reconfigurable architecture will also be leveraged to explore wake-up circuits that can be used in parallel with the buffer system

Floating-Gate Design and Linearization for Reconfigurable Analog Signal Processing

Analog and mixed-signal integrated circuits have found a place in modern electronics design as a viable alternative to digital pre-processing. With metrics that boast high accuracy and low power consumption, analog pre-processing has opened the door to low-power state-monitoring systems when it is utilized in place of a power-hungry digital signal-processing stage. However, the complicated design process required by analog and mixed-signal systems has been a barrier to broader applications. The implementation of floating-gate transistors has begun to pave the way for a more reasonable approach to analog design. Floating-gate technology has widespread use in the digital domain. Analog and mixed-signal use of floating-gate transistors has only become a rising field of study in recent years. Analog floating gates allow for low-power implementation of mixed-signal systems, such as the field-programmable analog array, while simultaneously opening the door to complex signal-processing techniques. The field-programmable analog array, which leverages floating-gate technologies, is demonstrated as a reliable replacement to signal-processing tasks previously only solved by custom design. Living in an analog world demands the constant use and refinement of analog signal processing for the purpose of interfacing with digital systems. This work offers a comprehensive look at utilizing floating-gate transistors as the core element for analog signal-processing tasks. This work demonstrates the floating gate\u27s merit in large reconfigurable array-driven systems and in smaller-scale implementations, such as linearization techniques for oscillators and analog-to-digital converters. A study on analog floating-gate reliability is complemented with a temperature compensation scheme for implementing these systems in ever-changing, realistic environments

Low-Power and Programmable Analog Circuitry for Wireless Sensors

Embedding networks of secure, wirelessly-connected sensors and actuators will help us to conscientiously manage our local and extended environments. One major challenge for this vision is to create networks of wireless sensor devices that provide maximal knowledge of their environment while using only the energy that is available within that environment. In this work, it is argued that the energy constraints in wireless sensor design are best addressed by incorporating analog signal processors. The low power-consumption of an analog signal processor allows persistent monitoring of multiple sensors while the device\u27s analog-to-digital converter, microcontroller, and transceiver are all in sleep mode. This dissertation describes the development of analog signal processing integrated circuits for wireless sensor networks. Specific technology problems that are addressed include reconfigurable processing architectures for low-power sensing applications, as well as the development of reprogrammable biasing for analog circuits

Low-Power and Programmable Analog Circuitry for Wireless Sensors

Embedding networks of secure, wirelessly-connected sensors and actuators will help us to conscientiously manage our local and extended environments. One major challenge for this vision is to create networks of wireless sensor devices that provide maximal knowledge of their environment while using only the energy that is available within that environment. In this work, it is argued that the energy constraints in wireless sensor design are best addressed by incorporating analog signal processors. The low power-consumption of an analog signal processor allows persistent monitoring of multiple sensors while the device\u27s analog-to-digital converter, microcontroller, and transceiver are all in sleep mode. This dissertation describes the development of analog signal processing integrated circuits for wireless sensor networks. Specific technology problems that are addressed include reconfigurable processing architectures for low-power sensing applications, as well as the development of reprogrammable biasing for analog circuits

Low-Power Reconfigurable Sensing Circuitry for the Internet-of-Things Paradigm

With ubiquitous wireless communication via Wi-Fi and nascent 5th Generation mobile communications, more devices -- both smart and traditionally dumb -- will be interconnected than ever before. This burgeoning trend is referred to as the Internet-of-Things. These new sensing opportunities place a larger burden on the underlying circuitry that must operate on finite battery power and/or within energy-constrained environments. New developments of low-power reconfigurable analog sensing platforms like field-programmable analog arrays (FPAAs) present an attractive sensing solution by processing data in the analog domain while staying flexible in design. This work addresses some of the contemporary challenges of low-power wireless sensing via traditional application-specific sensing and with FPAAs. A large emphasis is placed on furthering the development of FPAAs by making them more accessible to designers without a strong integrated-circuit background -- much like FPGAs have done for digital designers

Data Conversion Within Energy Constrained Environments

Within scientific research, engineering, and consumer electronics, there is a multitude of new discrete sensor-interfaced devices. Maintaining high accuracy in signal quantization while staying within the strict power-budget of these devices is a very challenging problem. Traditional paths to solving this problem include researching more energy-efficient digital topologies as well as digital scaling.;This work offers an alternative path to lower-energy expenditure in the quantization stage --- content-dependent sampling of a signal. Instead of sampling at a constant rate, this work explores techniques which allow sampling based upon features of the signal itself through the use of application-dependent analog processing. This work presents an asynchronous sampling paradigm, based off the use of floating-gate-enabled analog circuitry. The basis of this work is developed through the mathematical models necessary for asynchronous sampling, as well the SPICE-compatible models necessary for simulating floating-gate enabled analog circuitry. These base techniques and circuitry are then extended to systems and applications utilizing novel analog-to-digital converter topologies capable of leveraging the non-constant sampling rates for significant sample and power savings

FPGA-based architectures for acoustic beamforming with microphone arrays : trends, challenges and research opportunities

Over the past decades, many systems composed of arrays of microphones have been developed to satisfy the quality demanded by acoustic applications. Such microphone arrays are sound acquisition systems composed of multiple microphones used to sample the sound field with spatial diversity. The relatively recent adoption of Field-Programmable Gate Arrays (FPGAs) to manage the audio data samples and to perform the signal processing operations such as filtering or beamforming has lead to customizable architectures able to satisfy the most demanding computational, power or performance acoustic applications. The presented work provides an overview of the current FPGA-based architectures and how FPGAs are exploited for different acoustic applications. Current trends on the use of this technology, pending challenges and open research opportunities on the use of FPGAs for acoustic applications using microphone arrays are presented and discussed

Snowmass Topical Group Summary Report: IF07 -- ASICs and Electronics

This writeup summarizes the work of the Topical Working Group 7 of the Instrumentation Frontier group of the Snowmass 2021 process. Group 'IF07' dealt with issues pertaining to ASICs and Readout Electronics. The community efforts as part of IF07 were organized across 7 white papers submitted to the Snowmass process and available in the arXiv.Comment: arXiv admin note: substantial text overlap with arXiv:2209.1411

Mixed Signal Integrated Circuit Design for Custom Sensor Interfacing

Low-power analog integrated circuits (ICs) can be utilized at the interface between an analog sensor and a digital system\u27s input to decrease power consumption, increase system accuracy, perform signal processing, and make the necessary adjustments for compatibility between the two devices. This interfacing has typically been done with custom integrated solutions, but advancements in floating-gate technologies have made reconfigurable analog ICs a competitive option. Whether the solution is a custom design or built from a reconfigurable system, digital peripheral circuits are needed to configure their operation for these analog circuits to work with the best accuracy.;Using an analog IC as a front end signal processor between an analog sensor and wireless sensor mote can greatly decrease battery consumption. Processing in the digital domain requires more power than when done on an analog system. An Analog Signal Processor (ASP) can allow the digital wireless mote to remain in sleep mode while the ASP is always listening for an important event. Once this event occurs, the ASP will wake the wireless mote, allowing it to record the event and send radio transmissions if necessary. As most wireless sensor networks employ the use of batteries as a power source, an energy harvesting system in addition to an ASP can be used to further supplement this battery consumption.;This thesis documents the development of mixed-signal integrated circuits for use as interfaces between analog sensors and digital Wireless Sensor Networks (WSNs). The following work outlines, as well as shows the results, of development for sensor interfacing utilizing both custom mixed signal integrated circuits as well as a Field Programmable Analog Array (FPAA) for post fabrication customization. An Analog Signal Processor (ASP) has been used in an Acoustic Vehicle Classification system. To keep these interfacing methods low power, a prototype energy harvesting system using commercial-off-the-shelf (COTS) devices is detailed which has led to the design of a fully integrated solution

Strategies towards high performance (high-resolution/linearity) time-to-digital converters on field-programmable gate arrays

Time-correlated single-photon counting (TCSPC) technology has become popular in scientific research and industrial applications, such as high-energy physics, bio-sensing, non-invasion health monitoring, and 3D imaging. Because of the increasing demand for high-precision time measurements, time-to-digital converters (TDCs) have attracted attention since the 1970s. As a fully digital solution, TDCs are portable and have great potential for multichannel applications compared to bulky and expensive time-to-amplitude converters (TACs). A TDC can be implemented in ASIC and FPGA devices. Due to the low cost, flexibility, and short development cycle, FPGA-TDCs have become promising. Starting with a literature review, three original FPGA-TDCs with outstanding performance are introduced. The first design is the first efficient wave union (WU) based TDC implemented in Xilinx UltraScale (20 nm) FPGAs with a bubble-free sub-TDL structure. Combining with other existing methods, the resolution is further enhanced to 1.23 ps. The second TDC has been designed for LiDAR applications, especially in driver-less vehicles. Using the proposed new calibration method, the resolution is adjustable (50, 80, and 100 ps), and the linearity is exceptionally high (INL pk-pk and INL pk-pk are lower than 0.05 LSB). Meanwhile, a software tool has been open-sourced with a graphic user interface (GUI) to predict TDCs’ performance. In the third TDC, an onboard automatic calibration (AC) function has been realized by exploiting Xilinx ZYNQ SoC architectures. The test results show the robustness of the proposed method. Without the manual calibration, the AC function enables FPGA-TDCs to be applied in commercial products where mass production is required.Time-correlated single-photon counting (TCSPC) technology has become popular in scientific research and industrial applications, such as high-energy physics, bio-sensing, non-invasion health monitoring, and 3D imaging. Because of the increasing demand for high-precision time measurements, time-to-digital converters (TDCs) have attracted attention since the 1970s. As a fully digital solution, TDCs are portable and have great potential for multichannel applications compared to bulky and expensive time-to-amplitude converters (TACs). A TDC can be implemented in ASIC and FPGA devices. Due to the low cost, flexibility, and short development cycle, FPGA-TDCs have become promising. Starting with a literature review, three original FPGA-TDCs with outstanding performance are introduced. The first design is the first efficient wave union (WU) based TDC implemented in Xilinx UltraScale (20 nm) FPGAs with a bubble-free sub-TDL structure. Combining with other existing methods, the resolution is further enhanced to 1.23 ps. The second TDC has been designed for LiDAR applications, especially in driver-less vehicles. Using the proposed new calibration method, the resolution is adjustable (50, 80, and 100 ps), and the linearity is exceptionally high (INL pk-pk and INL pk-pk are lower than 0.05 LSB). Meanwhile, a software tool has been open-sourced with a graphic user interface (GUI) to predict TDCs’ performance. In the third TDC, an onboard automatic calibration (AC) function has been realized by exploiting Xilinx ZYNQ SoC architectures. The test results show the robustness of the proposed method. Without the manual calibration, the AC function enables FPGA-TDCs to be applied in commercial products where mass production is required