6T-SRAM 1Mb Design with Test Structures and Post Silicon Validation

abstract: Static random-access memories (SRAM) are integral part of design systems as caches and data memories that and occupy one-third of design space. The work presents an embedded low power SRAM on a triple well process that allows body-biasing control. In addition to the normal mode operation, the design is embedded with Physical Unclonable Function (PUF) [Suh07] and Sense Amplifier Test (SA Test) mode. With PUF mode structures, the fabrication and environmental mismatches in bit cells are used to generate unique identification bits. These bits are fixed and known as preferred state of an SRAM bit cell. The direct access test structure is a measurement unit for offset voltage analysis of sense amplifiers. These designs are manufactured using a foundry bulk CMOS 55 nm low-power (LP) process. The details about SRAM bit-cell and peripheral circuit design is discussed in detail, for certain cases the circuit simulation analysis is performed with random variations embedded in SPICE models. Further, post-silicon testing results are discussed for normal operation of SRAMs and the special test modes. The silicon and circuit simulation results for various tests are presented.Dissertation/ThesisMasters Thesis Electrical Engineering 201

Reliability models for dataflow computer systems

The demands for concurrent operation within a computer system and the representation of parallelism in programming languages have yielded a new form of program representation known as data flow (DENN 74, DENN 75, TREL 82a). A new model based on data flow principles for parallel computations and parallel computer systems is presented. Necessary conditions for liveness and deadlock freeness in data flow graphs are derived. The data flow graph is used as a model to represent asynchronous concurrent computer architectures including data flow computers

Functional visual sensitivity to ultraviolet wavelengths in the Pileated Woodpecker (\u3ci\u3eDryocopus pileatus\u3c/i\u3e), and its influence on foraging substrate selection

Most diurnal birds are presumed visually sensitive to near ultraviolet (UV)wavelengths, however, controlled behavioral studies investigating UV sensitivity remain few. Although woodpeckers are important as primary cavity excavators and nuisance animals, published work on their visual systems is limited. We developed a novel foraging-based behavioral assay designed to test UV sensitivity in the Pileated Woodpecker (Dryocopus pileatus). We acclimated 21 wild-caught woodpeckers to foraging for frozen mealworms within 1.2 m sections of peeled cedar (Thuja spp.) poles.We then tested the functional significance of UV cues by placing frozen mealworms behind UV-reflective covers, UV-absorptive covers, or decayed red pine substrates within the same 1.2 m poles in independent experiments. Behavioral responses were greater toward both UV-reflective and UV-absorptive substrates in three experiments. Study subjects therefore reliably differentiated and attended to two distinct UV conditions of a foraging substrate. Cue-naïve subjects showed a preference for UV-absorptive substrates, suggesting that woodpeckers may be pre-disposed to foraging from such substrates. Behavioral responses were greater toward decayed pine substrates (UV-reflective) than sound pine substrates suggesting that decayed pine can be a useful foraging cue. The finding that cue-naïve subjects selected UV-absorbing foraging substrates has implications for ecological interactions of woodpeckers with fungi.Woodpeckers transport fungal spores, and communication methods analogous to those of plant-pollinator mutualisms (i.e. UV-absorbing patterns) may have evolved to support woodpecker-fungus mutualisms

Deep learning: an introduction for applied mathematicians

Multilayered artificial neural networks are becoming a pervasive tool in a host of application fields. At the heart of this deep learning revolution are familiar concepts from applied and computational mathematics; notably, in calculus, approximation theory, optimization and linear algebra. This article provides a very brief introduction to the basic ideas that underlie deep learning from an applied mathematics perspective. Our target audience includes postgraduate and final year undergraduate students in mathematics who are keen to learn about the area. The article may also be useful for instructors in mathematics who wish to enliven their classes with references to the application of deep learning techniques. We focus on three fundamental questions: what is a deep neural network? how is a network trained? what is the stochastic gradient method? We illustrate the ideas with a short MATLAB code that sets up and trains a network. We also show the use of state-of-the art software on a large scale image classification problem. We finish with references to the current literature