20 research outputs found

    Time-encoding analog-to-digital converters : bridging the analog gap to advanced digital CMOS? Part 2: architectures and circuits

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    The scaling of CMOS technology deep into the nanometer range has created challenges for the design of highperformance analog ICs: they remain large in area and power consumption in spite of process scaling. Analog circuits based on time encoding [1], [2], where the signal information is encoded in the waveform transitions instead of its amplitude, have been developed to overcome these issues. While part one of this overview article [3] presented the basic principles of time encoding, this follow-up article describes and compares the main time-encoding architectures for analog-to-digital converters (ADCs) and discusses the corresponding design challenges of the circuit blocks. The focus is on structures that avoid, as much as possible, the use of traditional analog blocks like operational amplifiers (opamps) or comparators but instead use digital circuitry, ring oscillators, flip-flops, counters, an so on. Our overview of the state of the art will show that these circuits can achieve excellent performance. The obvious benefit of this highly digital approach to realizing analog functionality is that the resulting circuits are small in area and more compatible with CMOS process scaling. The approach also allows for the easy integration of these analog functions in systems on chip operating at "digital" supply voltages as low as 1V and lower. A large part of the design process can also be embedded in a standard digital synthesis flow

    An energy efficient noise-shaping SAR ADC in 28 nm FDSOI

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    In a noise-shaping SAR ADC, oversampling and noise shaping are used to increase the conversion accuracy beyond that the SAR exhibits alone. To implement the noise shaping, the residue voltage present at the SAR DAC plates after each conversion is exploited, and fed into a loop filter connected to an extra input of the SAR comparator. In this thesis, an energy efficient noise-shaping SAR ADC for medical ultrasound applications is designed in 28 nm FDSOI. The design specification is minimum 11.0 bit ENOB of accuracy, signal bandwidth of minimum 2 MHz, and sample rate of minimum 32MHz. According to post-layout Monte Carlo simulations, the designed ADC has an accuracy of 11.1 bit ENOB, and thus satisfies the accuracy requirement. The signal bandwidth and sample rate are the same as in the design specification. Specifically, the topics of this thesis are the design of the loop filter and its inter- facing towards the SAR, as well as the overall high level design. The 9-bit SAR used in the system is an already existing implementation. A cascaded FIR-IIR filter topology is used for the loop filter. In this work, the circuit implementation of this topology is improved, most importantly through the introduction of chopped buffers at the filter input. This eliminates signal attenuation due to charge sharing, and a DAC capacitance that is smaller than the sampling capacitance in the loop filter can therefore be used. Also, auto-zeroed, cascoded inverters rather than a standard OTA are used as gain elements in the switched-capacitor filter structure, and this leads to better energy efficiency. The designed ADC achieves a figure of merit (FOM) of 7.5fJ/conv-step in post-layout Monte Carlo simulations, and to the best of the author s knowledge, this is better than the current state-of-the-art of noise-shaping ADCs. When all kinds of ADCs are taken into consideration, the achieved FOM seems to be similar to the current state-of-the-art in the same specification range

    Analysis and design of low-power data converters

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    In a large number of applications the signal processing is done exploiting both analog and digital signal processing techniques. In the past digital and analog circuits were made on separate chip in order to limit the interference and other side effects, but the actual trend is to realize the whole elaboration chain on a single System on Chip (SoC). This choice is driven by different reasons such as the reduction of power consumption, less silicon area occupation on the chip and also reliability and repeatability. Commonly a large area in a SoC is occupied by digital circuits, then, usually a CMOS short-channel technological processes optimized to realize digital circuits is chosen to maximize the performance of the Digital Signal Proccessor (DSP). Opposite, the short-channel technology nodes do not represent the best choice for analog circuits. But in a large number of applications, the signals which are treated have analog nature (microphone, speaker, antenna, accelerometers, biopotential, etc.), then the input and output interfaces of the processing chip are analog/mixed-signal conversion circuits. Therefore in a single integrated circuit (IC) both digital and analog circuits can be found. This gives advantages in term of total size, cost and power consumption of the SoC. The specific characteristics of CMOS short-channel processes such as: • Low breakdown voltage (BV) gives a power supply limit (about 1.2 V). • High threshold voltage VTH (compared with the available voltage supply) fixed in order to limit the leakage power consumption in digital applications (of the order of 0.35 / 0.4V), puts a limit on the voltage dynamic, and creates many problems with the stacked topologies. • Threshold voltage dependent on the channel length VTH = f(L) (short channel effects). • Low value of the output resistance of the MOS (r0) and gm limited by speed saturation, both causes contribute to achieving a low intrinsic gain gmr0 = 20 to 26dB. • Mismatch which brings offset effects on analog circuits. make the design of high performance analog circuits very difficult. Realizing lowpower circuits is fundamental in different contexts, and for different reasons: lowering the power dissipation gives the capability to reduce the batteries size in mobile devices (laptops, smartphones, cameras, measuring instruments, etc.), increase the life of remote sensing devices, satellites, space probes, also allows the reduction of the size and weight of the heat sink. The reduction of power dissipation allows the realization of implantable biomedical devices that do not damage biological tissue. For this reason, the analysis and design of low power and high precision analog circuits is important in order to obtain high performance in technological processes that are not optimized for such applications. Different ways can be taken to reduce the effect of the problems related to the technology: • Circuital level: a circuit-level intervention is possible to solve a specific problem of the circuit (i.e. Techniques for bandwidth expansion, increase the gain, power reduction, etc.). • Digital calibration: it is the highest level to intervene, and generally going to correct the non-ideal structure through a digital processing, these aims are based on models of specific errors of the structure. • Definition of new paradigms. This work has focused the attention on a very useful mixed-signal circuit: the pipeline ADC. The pipeline ADCs are widely used for their energy efficiency in high-precision applications where a resolution of about 10-16 bits and sampling rates above hundreds of Mega-samples per second (telecommunication, radar, etc.) are needed. An introduction on the theory of pipeline ADC, its state of the art and the principal non-idealities that affect the energy efficiency and the accuracy of this kind of data converters are reported in Chapter 1. Special consideration is put on low-voltage low-power ADCs. In particular, for ADCs implemented in deep submicron technology nodes side effects called short channel effects exist opposed to older technology nodes where undesired effects are not present. An overview of the short channel effects and their consequences on design, and also power consuption reduction techniques, with particular emphasis on the specific techniques adopted in pipelined ADC are reported in Chapter 2. Moreover, another way may be undertaken to increase the accuracy and the efficiency of an ADC, this way is the digital calibration. In Chapter 3 an overview on digital calibration techniques, and furthermore a new calibration technique based on Volterra kernels are reported. In some specific applications, such as software defined radios or micropower sensor, some circuits should be reconfigurable to be suitable for different radio standard or process signals with different charateristics. One of this building blocks is the ADC that should be able to reconfigure the resolution and conversion frequency. A reconfigurable voltage-scalable ADC pipeline capable to adapt its voltage supply starting from the required conversion frequency was developed, and the results are reported in Chapter 4. In Chapter 5, a pipeline ADC based on a novel paradigm for the feedback loop and its theory is described

    Ultra-low Power Circuits and Architectures for Neuromorphic Computing Accelerators with Emerging TFETs and ReRAMs

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    Neuromorphic computing using post-CMOS technologies is gaining increasing popularity due to its promising potential to resolve the power constraints in Von-Neumann machine and its similarity to the operation of the real human brain. To design the ultra-low voltage and ultra-low power analog-to-digital converters (ADCs) for the neuromorphic computing systems, we explore advantages of tunnel field effect transistor (TFET) analog-to-digital converters (ADCs) on energy efficiency and temperature stability. A fully-differential SAR ADC is designed using 20 nm TFET technology with doubled input swing and controlled comparator input common-mode voltage. To further increase the resolution of the ADC, we design an energy efficient 12-bit noise shaping (NS) successive-approximation register (SAR) ADC. The 2nd-order noise shaping architecture with multiple feed-forward paths is adopted and analyzed to optimize system design parameters. By utilizing tunnel field effect transistors (TFETs), the Delta-Sigma SAR is realized under an ultra-low supply voltage VDD with high energy efficiency. The stochastic neuron is a key for event-based probabilistic neural networks. We propose a stochastic neuron using a metal-oxide resistive random-access memory (ReRAM). The ReRAM\u27s conducting filament with built-in stochasticity is used to mimic the neuron\u27s membrane capacitor, which temporally integrates input spikes. A capacitor-less neuron circuit is designed, laid out, and simulated. The output spiking train of the neuron obeys the Poisson distribution. Based on the ReRAM based neuron, we propose a scalable and reconfigurable architecture that exploits the ReRAM-based neurons for deep Spiking Neural Networks (SNNs). In prior publications, neurons were implemented using dedicated analog or digital circuits that are not area and energy efficient. In our work, for the first time, we address the scaling and power bottlenecks of neuromorphic architecture by utilizing a single one-transistor-one-ReRAM (1T1R) cell to emulate the neuron. We show that the ReRAM-based neurons can be integrated within the synaptic crossbar to build extremely dense Process Element (PE)–spiking neural network in memory array–with high throughput. We provide microarchitecture and circuit designs to enable the deep spiking neural network computing in memory with an insignificant area overhead
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