Design of Two Phase Sinusoidal Power Clock using Adiabatic Switching

"Adiabatic" is a term of Greek origin that has spent most of its history associated with classical thermodynamics. It refers to a system in which a transition occurs without energy usually in the form of heat being either lost to or gained from the system. In the context of electronic systems, rather than heat, electronic charge is preserved. Thus, an ideal adiabatic circuit would operate without the loss or gain of electronic charge. Hence, in this work the two phase sinusoidal power clock is designed using Adiabatic switching

Delay Measurements and Self Characterisation on FPGAs

This thesis examines new timing measurement methods for self delay characterisation of Field-Programmable Gate Arrays (FPGAs) components and delay measurement of complex circuits on FPGAs. Two novel measurement techniques based on analysis of a circuit's output failure rate and transition probability is proposed for accurate, precise and efficient measurement of propagation delays. The transition probability based method is especially attractive, since it requires no modifications in the circuit-under-test and requires little hardware resources, making it an ideal method for physical delay analysis of FPGA circuits. The relentless advancements in process technology has led to smaller and denser transistors in integrated circuits. While FPGA users benefit from this in terms of increased hardware resources for more complex designs, the actual productivity with FPGA in terms of timing performance (operating frequency, latency and throughput) has lagged behind the potential improvements from the improved technology due to delay variability in FPGA components and the inaccuracy of timing models used in FPGA timing analysis. The ability to measure delay of any arbitrary circuit on FPGA offers many opportunities for on-chip characterisation and physical timing analysis, allowing delay variability to be accurately tracked and variation-aware optimisations to be developed, reducing the productivity gap observed in today's FPGA designs. The measurement techniques are developed into complete self measurement and characterisation platforms in this thesis, demonstrating their practical uses in actual FPGA hardware for cross-chip delay characterisation and accurate delay measurement of both complex combinatorial and sequential circuits, further reinforcing their positions in solving the delay variability problem in FPGAs

Optimal transient stability control of synchronous generators by on-line digital computer

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Design-for-delay-testability techniques for high-speed digital circuits

The importance of delay faults is enhanced by the ever increasing clock rates and decreasing geometry sizes of nowadays' circuits. This thesis focuses on the development of Design-for-Delay-Testability (DfDT) techniques for high-speed circuits and embedded cores. The rising costs of IC testing and in particular the costs of Automatic Test Equipment are major concerns for the semiconductor industry. To reverse the trend of rising testing costs, DfDT is\ud getting more and more important

Processing and Transmission of Information

Contains reports on six research projects.Purchase Order DDL-B15

A Multi-Gigahertz Analog Transient Recorder Integrated Circuit

A monolithic multi-channel analog transient recorder, implemented using switched capacitor sample-and-hold circuits and a high-speed analogically-adjustable delay-line-based write clock, has been designed, fabricated and tested. The 2.1 by 6.9 mm layout, in 1.2 micron CMOS, includes over 31,000 transistors and 2048 double polysilicon capacitors. The circuit contains four parallel channels, each with a 512 deep switched-capacitor sample-and-hold system. A 512 deep edge sensitive tapped active delay line uses look-ahead and 16 way interleaving to develop the 512 sample and hold clocks, each as little as 3.2 ns wide and 200 ps apart. Measurements of the device have demonstrated 5 GHz maximum sample rate, at least 350 MHz bandwidth, an extrapolated rms aperture uncertainty per sample of 0.7 ps, and a signal to rms noise ratio of 2000:1.Comment: 64 pages, 17 figures. Thesis, University of California, Berkeley, 199

Acetylcholine neuromodulation in normal and abnormal learning and memory: vigilance control in waking, sleep, autism, amnesia, and Alzheimer's disease

This article provides a unified mechanistic neural explanation of how learning, recognition, and cognition break down during Alzheimer's disease, medial temporal amnesia, and autism. It also clarifies whey there are often sleep disturbances during these disorders. A key mechanism is how acetylcholine modules vigilance control in cortical layer

Dynamical principles in neuroscience

Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?This work was supported by NSF Grant No. NSF/EIA-0130708, and Grant No. PHY 0414174; NIH Grant No. 1 R01 NS50945 and Grant No. NS40110; MEC BFI2003-07276, and Fundación BBVA