97 research outputs found

    Design and debugging of multi-step analog to digital converters

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    With the fast advancement of CMOS fabrication technology, more and more signal-processing functions are implemented in the digital domain for a lower cost, lower power consumption, higher yield, and higher re-configurability. The trend of increasing integration level for integrated circuits has forced the A/D converter interface to reside on the same silicon in complex mixed-signal ICs containing mostly digital blocks for DSP and control. However, specifications of the converters in various applications emphasize high dynamic range and low spurious spectral performance. It is nontrivial to achieve this level of linearity in a monolithic environment where post-fabrication component trimming or calibration is cumbersome to implement for certain applications or/and for cost and manufacturability reasons. Additionally, as CMOS integrated circuits are accomplishing unprecedented integration levels, potential problems associated with device scaling – the short-channel effects – are also looming large as technology strides into the deep-submicron regime. The A/D conversion process involves sampling the applied analog input signal and quantizing it to its digital representation by comparing it to reference voltages before further signal processing in subsequent digital systems. Depending on how these functions are combined, different A/D converter architectures can be implemented with different requirements on each function. Practical realizations show the trend that to a first order, converter power is directly proportional to sampling rate. However, power dissipation required becomes nonlinear as the speed capabilities of a process technology are pushed to the limit. Pipeline and two-step/multi-step converters tend to be the most efficient at achieving a given resolution and sampling rate specification. This thesis is in a sense unique work as it covers the whole spectrum of design, test, debugging and calibration of multi-step A/D converters; it incorporates development of circuit techniques and algorithms to enhance the resolution and attainable sample rate of an A/D converter and to enhance testing and debugging potential to detect errors dynamically, to isolate and confine faults, and to recover and compensate for the errors continuously. The power proficiency for high resolution of multi-step converter by combining parallelism and calibration and exploiting low-voltage circuit techniques is demonstrated with a 1.8 V, 12-bit, 80 MS/s, 100 mW analog to-digital converter fabricated in five-metal layers 0.18-µm CMOS process. Lower power supply voltages significantly reduce noise margins and increase variations in process, device and design parameters. Consequently, it is steadily more difficult to control the fabrication process precisely enough to maintain uniformity. Microscopic particles present in the manufacturing environment and slight variations in the parameters of manufacturing steps can all lead to the geometrical and electrical properties of an IC to deviate from those generated at the end of the design process. Those defects can cause various types of malfunctioning, depending on the IC topology and the nature of the defect. To relive the burden placed on IC design and manufacturing originated with ever-increasing costs associated with testing and debugging of complex mixed-signal electronic systems, several circuit techniques and algorithms are developed and incorporated in proposed ATPG, DfT and BIST methodologies. Process variation cannot be solved by improving manufacturing tolerances; variability must be reduced by new device technology or managed by design in order for scaling to continue. Similarly, within-die performance variation also imposes new challenges for test methods. With the use of dedicated sensors, which exploit knowledge of the circuit structure and the specific defect mechanisms, the method described in this thesis facilitates early and fast identification of excessive process parameter variation effects. The expectation-maximization algorithm makes the estimation problem more tractable and also yields good estimates of the parameters for small sample sizes. To allow the test guidance with the information obtained through monitoring process variations implemented adjusted support vector machine classifier simultaneously minimize the empirical classification error and maximize the geometric margin. On a positive note, the use of digital enhancing calibration techniques reduces the need for expensive technologies with special fabrication steps. Indeed, the extra cost of digital processing is normally affordable as the use of submicron mixed signal technologies allows for efficient usage of silicon area even for relatively complex algorithms. Employed adaptive filtering algorithm for error estimation offers the small number of operations per iteration and does not require correlation function calculation nor matrix inversions. The presented foreground calibration algorithm does not need any dedicated test signal and does not require a part of the conversion time. It works continuously and with every signal applied to the A/D converter. The feasibility of the method for on-line and off-line debugging and calibration has been verified by experimental measurements from the silicon prototype fabricated in standard single poly, six metal 0.09-µm CMOS process

    Analysis and Design of High-Speed A/D Converters in SiGe Technology

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    Mixed-signal systems play a key role in modern communications and electronics. The quality of A/D and D/A conversions deeply affects what we see and what we hear in the real world video and radio. This dissertation deals with high-speed ADCs: a 5-bit 500-MSPS ADC and an 8-bit 2-GSPS ADC. These units can be applied in flat panel display, image enhancement and in high-speed data link. To achieve the state-of-the-art performance, we employed a 0.13-ÎĽm/2.5-V 210-GHz (unity-gain frequency) BiCMOS SiGe process for all the implementations. The circuit building blocks, such as the Track-and-Hold circuit (T/H) and the comparator, required by an ADC not only benefit from SiGe's superior ultra-high frequency properties but also by its power drive capability. The T/H described here achieved a dynamic performance of 8-bit accuracy at 2-GHz Nyquist rate with an input full scale range of 1 Vp-p. The T/H consumed 13 mW of power. The unique 4-in/2-out comparator was made of fully differential emitter couple pairs in order to operate at such a high frequency. Cascaded cross-coupled amplifier core was employed to reduce Miller effect and to avoid collector-emitter breakdown of the HBTs. We utilized the comparator interpolation technique between the preamplifer stages and the latches to reduce the total power dissipated by the comparator array. In addition, we developed an innovative D/A conversion and analog subtraction approach necessary for two-step conversion by using a bipolar pre-distortion technique. This innovation enabled us to decrease the design complexity in the subranging process of a two-step ADC. The 5-bit interpolating ADC operated at 2-GSPS achieved a differential nonlinearity (DNL) of 0.114 LSB and an integral nonlinearity (INL) of 0.076 LSB. The effective number of bits (ENOBs) are 4.3 bits at low frequency and 4.1 bits near Nyquist rate. The power dissipation was reduced more than half to 66.14 mW, with comparator interpolation. The 8-bit two-step interpolating ADC operated at 500-MSPS. It achieved a DNL of 0.33 LSB and an INL of 0.40 LSB with a power consumption of 172 mW. The ENOBs are 7.5 bits at low frequency and 6.9 bits near Nyquist rate

    480-GMACS/mW Resonant Adiabatic Mixed-Signal Processor Array for Charge-Based Pattern Recognition

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    Hardware Learning in Analogue VLSI Neural Networks

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    Mixed-mode cellular array processor realization for analyzing brain electrical activity in epilepsy

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    This thesis deals with the realization of hardware that is capable of computing algorithms that can be described using the theory of polynomial cellular neural/nonlinear networks (CNNs). The goal is to meet the requirements of an algorithm for predicting the onset of an epileptic seizure. The analysis associated with this application requires extensive computation of data that consists of segments of brain electrical activity. Different types of computer architectures are overviewed. Since the algorithm requires operations in which data is manipulated locally, special emphasis is put on assessing different parallel architectures. An array computer is potentially able to perform local computational tasks effectively and rapidly. Based on the requirements of the algorithm, a mixed-mode CNN is proposed. A mixed-mode CNN combines analog and digital processing so that the couplings and the polynomial terms are implemented with analog blocks, whereas the integrator is digital. A/D and D/A converters are used to interface between the analog blocks and the integrator. Based on the mixed-mode CNN architecture a cellular array processor is realized. In the realized array processor the processing units are coupled with programmable polynomial (linear, quadratic and cubic) first neighborhood feedback terms. A 10 mm2, 1.027 million transistor cellular array processor, with 2×72 processing units and 36 layers of memory in each is manufactured using a 0.25 μm digital CMOS process. The array processor can perform gray-scale Heun's integration of spatial convolutions with linear, quadratic and cubic activation functions for 72×72 data while keeping all I/O operations during processing local. One complete Heun's iteration round takes 166.4 μs, while the power consumption during processing is 192 mW. Experimental results of statistical variations in the multipliers and polynomial circuits are shown. Descriptions regarding improvements in the design are also explained. The results of this thesis can be used to assess the suitability of the mixed-mode approach for implementing an implantable system for predicting epileptic seizures. The results can also be used to assess the suitability of the approach for implementing other applications.reviewe

    Toward a Distributed Actuation and Cognition Means for a Miniature Soft Robot

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    This thesis presents components of an on-going research project aimed towards developing a miniature soft robot for urban search and rescue (USAR). The three significant contributions of the thesis are verifying the water hammer actuation previous work, developing an estimator of water hammer impulse direction from hose shape, and creating the infrastructure for distributed cognitive networks. There are many technical issues in designing soft robots, in terms of perception, actuation, cognition, power, physical structure and so on. We are focusing on actuation and cognition issues in this thesis. We investigated water hammer actuation as an alternative system which provides a continuously distributed form of actuation results from water hammer effect. It is special because it is a soft actuation method. We generated some comparison experiments and verified the benefits of the water hammer actuation, and also designed our soft robot to be hose-like in order to utilize the water hammer actuator. For the cognition part, we first addressed and verified that the shape of the hose-like robot has impact on impulse direction from the water hammer actuation. And then we implemented an emulated synthetic neural network (ESNN) to analyze the direction of the impulse from the water hammer actuation. Then in order to achieve the long-term goal, we distributed the emulated synthetic neural network onto many embedded system boards to achieve a distributed cognitive network. The distributed nodes in the network are using Bluetooth communication. In the comparison experiments between the active tether system and passive tether system, we can clearly see the benefits of active tether in momentum transfer and friction reduction. For example, in the drag test, with the water hammer actuation the burden that the tether can pull was increased by about 1.6 times. For the distributed cognitive network, we successfully built an emulated synthetic neural network on distributed embedded system boards. With the shape information as the inputs, the difference on outputs from the ESNN and the experimental results is less than 3%

    Project and development of hardware accelerators for fast computing in multimedia processing

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    2017 - 2018The main aim of the present research work is to project and develop very large scale electronic integrated circuits, with particular attention to the ones devoted to image processing applications and the related topics. In particular, the candidate has mainly investigated four topics, detailed in the following. First, the candidate has developed a novel multiplier circuit capable of obtaining floating point (FP32) results, given as inputs an integer value from a fixed integer range and a set of fixed point (FI) values. The result has been accomplished exploiting a series of theorems and results on a number theory problem, known as Bachet’s problem, which allows the development of a new Distributed Arithmetic (DA) based on 3’s partitions. This kind of application results very fit for filtering applications working on an integer fixed input range, such in image processing applications, in which the pixels are coded on 8 bits per channel. In fact, in these applications the main problem is related to the high area and power consumption due to the presence of many Multiply and Accumulate (MAC) units, also compromising real-time requirements due to the complexity of FP32 operations. For these reasons, FI implementations are usually preferred, at the cost of lower accuracies. The results for the single multiplier and for a filter of dimensions 3x3 show respectively delay of 2.456 ns and 4.7 ns on FPGA platform and 2.18 ns and 4.426 ns on 90nm std_cell TSMC 90 nm implementation. Comparisons with state-of-the-art FP32 multipliers show a speed increase of up to 94.7% and an area reduction of 69.3% on FPGA platform. ... [edited by Author]XXXI cicl
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