Wouter A. Serdijn

Bio: Wouter A. Serdijn is an academic researcher from Delft University of Technology. The author has contributed to research in topics: CMOS & Noise figure. The author has an hindex of 33, co-authored 320 publications receiving 4441 citations. Previous affiliations of Wouter A. Serdijn include University College London & National Semiconductor.

Co-Design of a CMOS Rectifier and Small Loop Antenna for Highly Sensitive RF Energy Harvesters

Abstract: In this paper, a design method for the co-design and integration of a CMOS rectifier and small loop antenna is described. In order to improve the sensitivity, the antenna-rectifier interface is analyzed as it plays a crucial role in the co-design optimization. Subsequently, a 5-stage cross-connected differential rectifier with a 7-bit binary-weighted capacitor bank is designed and fabricated in standard 90 nm CMOS technology. The rectifier is brought at resonance with a high-Q loop antenna by means of a control loop that compensates for any variation at the antenna-rectifier interface and passively boosts the antenna voltage to enhance the sensitivity. A complementary MOS diode is proposed to improve the harvester's ability to store and hold energy over a long period of time during which there is insufficient power for rectification. The chip is ESD protected and integrated on a compact loop antenna. Measurements in an anechoic chamber at 868 MHz demonstrate a -27 dBm sensitivity for 1 V output across a capacitive load and 27 meter range for a 1.78 W RF source in an office corridor. The end-to-end power conversion efficiency equals 40% at -17 dBm.

Analysis of Power Consumption and Linearity in Capacitive Digital-to-Analog Converters Used in Successive Approximation ADCs

Abstract: Successive-approximation analog-to-digital converters (SA-ADCs) are widely used in ultra-low-power applications. In this paper, the power consumption and the linearity of capacitive-array digital-to-analog converters (DACs) employed in SA-ADCs are analyzed. Specifically, closed-form formulas for the power consumption as well as the standard deviation of INL and DNL for three commonly-used radix-2 architectures including the effect of parasitic capacitances are presented and the structures are compared. The proposed analysis can be employed in choosing the best architecture and optimizing it in both hand calculations and computer-aided-design tools. Measurement results of previously published works as well as simulation results of a 10-bit 10 kS/s SA-ADC confirm the accuracy of the proposed equations. It will be shown that, in spite of what commonly is assumed, although the total capacitance and the power consumption of those architectures employing attenuating capacitors seem to be smaller than conventional binary-weighted structures, the linearity requirements impose much larger unit capacitance to the structure such that the entire power consumption is larger.

Cerebellar Output Controls Generalized Spike-and-Wave Discharge Occurrence

Abstract: Objective Disrupting thalamocortical activity patterns has proven to be a promising approach to stop generalized spike-and-wave discharges (GSWDs) characteristic of absence seizures. Here, we investigated to what extent modulation of neuronal firing in cerebellar nuclei (CN), which are anatomically in an advantageous position to disrupt cortical oscillations through their innervation of a wide variety of thalamic nuclei, is effective in controlling absence seizures. Methods Two unrelated mouse models of generalized absence seizures were used: the natural mutant tottering, which is characterized by a missense mutation in Cacna1a, and inbred C3H/HeOuJ. While simultaneously recording single CN neuron activity and electrocorticogram in awake animals, we investigated to what extent pharmacologically increased or decreased CN neuron activity could modulate GSWD occurrence as well as short-lasting, on-demand CN stimulation could disrupt epileptic seizures. Results We found that a subset of CN neurons show phase-locked oscillatory firing during GSWDs and that manipulating this activity modulates GSWD occurrence. Inhibiting CN neuron action potential firing by local application of the ?-aminobutyric acid type A (GABA-A) agonist muscimol increased GSWD occurrence up to 37-fold, whereas increasing the frequency and regularity of CN neuron firing with the use of GABA-A antagonist gabazine decimated its occurrence. A single short-lasting (30–300 milliseconds) optogenetic stimulation of CN neuron activity abruptly stopped GSWDs, even when applied unilaterally. Using a closed-loop system, GSWDs were detected and stopped within 500 milliseconds. Interpretation CN neurons are potent modulators of pathological oscillations in thalamocortical network activity during absence seizures, and their potential therapeutic benefit for controlling other types of generalized epilepsies should be evaluated. Ann Neurol 2015;77:1027–1049

A Sub-Microwatt Asynchronous Level-Crossing ADC for Biomedical Applications

Abstract: A continuous-time level-crossing analog-to-digital converter (LC-ADC) for biomedical applications is presented. When compared to uniform-sampling (US) ADCs LC-ADCs generate fewer samples for various sparse biomedical signals. Lower power consumption and reduced design complexity with respect to conventional LC-ADCs are achieved due to: 1) replacing the n-bit digital-to-analog converter (DAC) with a 1-bit DAC; 2) splitting the level-crossing detections; and 3) fixing the comparison window. Designed and implemented in 0.18 μm CMOS technology, the proposed ADC uses a chip area of 220 × 203 μm2. Operating from a supply voltage of 0.8 V, the ADC consumes 313-582 nW from 5 Hz to 5 kHz and achieves an ENOB up to 7.9 bits.

Wireless Networks With RF Energy Harvesting: A Contemporary Survey

Abstract: Radio frequency (RF) energy transfer and harvesting techniques have recently become alternative methods to power the next-generation wireless networks As this emerging technology enables proactive energy replenishment of wireless devices, it is advantageous in supporting applications with quality-of-service requirements In this paper, we present a comprehensive literature review on the research progresses in wireless networks with RF energy harvesting capability, which is referred to as RF energy harvesting networks (RF-EHNs) First, we present an overview of the RF-EHNs including system architecture, RF energy harvesting techniques, and existing applications Then, we present the background in circuit design as well as the state-of-the-art circuitry implementations and review the communication protocols specially designed for RF-EHNs We also explore various key design issues in the development of RF-EHNs according to the network types, ie, single-hop networks, multiantenna networks, relay networks, and cognitive radio networks Finally, we envision some open research directions

Linear systems

Abstract: Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

ISAAC: a convolutional neural network accelerator with in-situ analog arithmetic in crossbars

Abstract: A number of recent efforts have attempted to design accelerators for popular machine learning algorithms, such as those involving convolutional and deep neural networks (CNNs and DNNs). These algorithms typically involve a large number of multiply-accumulate (dot-product) operations. A recent project, DaDianNao, adopts a near data processing approach, where a specialized neural functional unit performs all the digital arithmetic operations and receives input weights from adjacent eDRAM banks.This work explores an in-situ processing approach, where memristor crossbar arrays not only store input weights, but are also used to perform dot-product operations in an analog manner. While the use of crossbar memory as an analog dot-product engine is well known, no prior work has designed or characterized a full-fledged accelerator based on crossbars. In particular, our work makes the following contributions: (i) We design a pipelined architecture, with some crossbars dedicated for each neural network layer, and eDRAM buffers that aggregate data between pipeline stages. (ii) We define new data encoding techniques that are amenable to analog computations and that can reduce the high overheads of analog-to-digital conversion (ADC). (iii) We define the many supporting digital components required in an analog CNN accelerator and carry out a design space exploration to identify the best balance of memristor storage/compute, ADCs, and eDRAM storage on a chip. On a suite of CNN and DNN workloads, the proposed ISAAC architecture yields improvements of 14.8×, 5.5×, and 7.5× in throughput, energy, and computational density (respectively), relative to the state-of-the-art DaDianNao architecture.

Design Of Analog Cmos Integrated Circuits

Abstract: Thank you for downloading design of analog cmos integrated circuits. Maybe you have knowledge that, people have look hundreds times for their chosen books like this design of analog cmos integrated circuits, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some harmful virus inside their computer. design of analog cmos integrated circuits is available in our book collection an online access to it is set as public so you can download it instantly. Our digital library spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the design of analog cmos integrated circuits is universally compatible with any devices to read.

Design Of Analog Cmos Integrated Circuits

Abstract: Thank you very much for downloading design of analog cmos integrated circuits. Maybe you have knowledge that, people have look hundreds times for their favorite novels like this design of analog cmos integrated circuits, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some malicious virus inside their laptop. design of analog cmos integrated circuits is available in our book collection an online access to it is set as public so you can download it instantly. Our digital library saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the design of analog cmos integrated circuits is universally compatible with any devices to read.