Power-efficient current-mode analog circuits for highly integrated ultra low power wireless transceivers

In this thesis, current-mode low-voltage and low-power techniques have been applied to implement novel analog circuits for zero-IF receiver backend design, focusing on amplification, filtering and detection stages. The structure of the thesis follows a bottom-up scheme: basic techniques at device level for low voltage low power operation are proposed in the first place, followed by novel circuit topologies at cell level, and finally the achievement of new designs at system level. At device level the main contribution of this work is the employment of Floating-Gate (FG) and Quasi-Floating-Gate (QFG) transistors in order to reduce the power consumption. New current-mode basic topologies are proposed at cell level: current mirrors and current conveyors. Different topologies for low-power or high performance operation are shown, being these circuits the base for the system level designs. At system level, novel current-mode amplification, filtering and detection stages using the former mentioned basic cells are proposed. The presented current-mode filter makes use of companding techniques to achieve high dynamic range and very low power consumption with for a very wide tuning range. The amplification stage avoids gain bandwidth product achieving a constant bandwidth for different gain configurations using a non-linear active feedback network, which also makes possible to tune the bandwidth. Finally, the proposed current zero-crossing detector represents a very power efficient mixed signal detector for phase modulations. All these designs contribute to the design of very low power compact Zero-IF wireless receivers. The proposed circuits have been fabricated using a 0.5μm double-poly n-well CMOS technology, and the corresponding measurement results are provided and analyzed to validate their operation. On top of that, theoretical analysis has been done to fully explore the potential of the resulting circuits and systems in the scenario of low-power low-voltage applications.Programa Oficial de Doctorado en Tecnologías de las Comunicaciones (RD 1393/2007)Komunikazioen Teknologietako Doktoretza Programa Ofiziala (ED 1393/2007

A neural probe with up to 966 electrodes and up to 384 configurable channels in 0.13 μm SOI CMOS

In vivo recording of neural action-potential and local-field-potential signals requires the use of high-resolution penetrating probes. Several international initiatives to better understand the brain are driving technology efforts towards maximizing the number of recording sites while minimizing the neural probe dimensions. We designed and fabricated (0.13-μm SOI Al CMOS) a 384-channel configurable neural probe for large-scale in vivo recording of neural signals. Up to 966 selectable active electrodes were integrated along an implantable shank (70 μm wide, 10 mm long, 20 μm thick), achieving a crosstalk of −64.4 dB. The probe base (5 × 9 mm2) implements dual-band recording and a 1

A Versatile Active Block: DXCCCII and Tunable Applications

The study describes dual-X controlled current conveyor (DXCCCII) as a versatile active block and its application to inductance simulators for testing. Moreover, the high pass filter application using with DXCCCII based inductance simulator and oscillator with flexible tunable oscillation frequency have been presented and simulated to confirm the theoretical validity. The proposed circuit which has a simple circuit design requires the low-voltage and the DXCCCII can also be tuned in the wide range by the biasing current. The proposed DXCCCII provides a good linearity, high output impedance at Z terminals, and a reasonable current and voltage transfer gain accuracy. The proposed DXCCCII and its applications have been simulated using the CMOS 0.18 µm technology

FVF-Based Low-Dropout Voltage Regulator with Fast Charging/Discharging Paths for Fast Line and Load Regulation

A new internally compensated low drop-out voltage regulator based on the cascoded flipped voltage follower is presented in this paper. Adaptive biasing current and fast charging/discharging paths have been added to rapidly charge and discharge the parasitic capacitance of the pass transistor gate, thus improving the transient response. The proposed regulator was designed with standard 65-nm CMOS technology. Measurements show load and line regulations of 433.80 μV/mA and 5.61 mV/V, respectively. Furthermore, the output voltage spikes are kept under 76 mV for 0.1 mA to 100 mA load variations and 0.9 V to 1.2 V line variations with rise and fall times of 1 μs. The total current consumption is 17.88 μA (for a 0.9 V supply voltage).Ministerio de Economía y Competitividad TEC2015-71072-C3-3-RConsejería de Economía, Innovación y Ciencia. Junta de Andalucía P12-TIC-186

An Analog VLSI Deep Machine Learning Implementation

Machine learning systems provide automated data processing and see a wide range of applications. Direct processing of raw high-dimensional data such as images and video by machine learning systems is impractical both due to prohibitive power consumption and the “curse of dimensionality,” which makes learning tasks exponentially more difficult as dimension increases. Deep machine learning (DML) mimics the hierarchical presentation of information in the human brain to achieve robust automated feature extraction, reducing the dimension of such data. However, the computational complexity of DML systems limits large-scale implementations in standard digital computers. Custom analog signal processing (ASP) can yield much higher energy efficiency than digital signal processing (DSP), presenting means of overcoming these limitations. The purpose of this work is to develop an analog implementation of DML system. First, an analog memory is proposed as an essential component of the learning systems. It uses the charge trapped on the floating gate to store analog value in a non-volatile way. The memory is compatible with standard digital CMOS process and allows random-accessible bi-directional updates without the need for on-chip charge pump or high voltage switch. Second, architecture and circuits are developed to realize an online k-means clustering algorithm in analog signal processing. It achieves automatic recognition of underlying data pattern and online extraction of data statistical parameters. This unsupervised learning system constitutes the computation node in the deep machine learning hierarchy. Third, a 3-layer, 7-node analog deep machine learning engine is designed featuring online unsupervised trainability and non-volatile floating-gate analog storage. It utilizes massively parallel reconfigurable current-mode analog architecture to realize efficient computation. And algorithm-level feedback is leveraged to provide robustness to circuit imperfections in analog signal processing. At a processing speed of 8300 input vectors per second, it achieves 1×1012 operation per second per Watt of peak energy efficiency. In addition, an ultra-low-power tunable bump circuit is presented to provide similarity measures in analog signal processing. It incorporates a novel wide-input-range tunable pseudo-differential transconductor. The circuit demonstrates tunability of bump center, width and height with a power consumption significantly lower than previous works