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000696248 020__ $$a9789400777811 $$qelectronic book
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000696248 0247_ $$a10.1007/978-94-007-7781-1$$2doi
000696248 035__ $$aSP(OCoLC)ocn867641450
000696248 035__ $$aSP(OCoLC)867641450
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000696248 050_4 $$aTK7871.99.M44
000696248 08204 $$a621.3815/2$$223
000696248 1001_ $$aZjajo, Amir,$$eauthor.
000696248 24510 $$aStochastic process variation in deep-submicron CMOS$$h[electronic resource] :$$bcircuits and algorithms /$$cAmir Zjajo.
000696248 264_1 $$aDordrecht :$$bSpringer,$$c2014.
000696248 300__ $$a1 online resource (xix, 192 pages) :$$billustrations.
000696248 336__ $$atext$$btxt$$2rdacontent
000696248 337__ $$acomputer$$bc$$2rdamedia
000696248 338__ $$aonline resource$$bcr$$2rdacarrier
000696248 4901_ $$aSpringer Series in Advanced Microelectronics,$$x1437-0387 ;$$vvolume 48
000696248 504__ $$aIncludes bibliographical references and index.
000696248 5050_ $$aRandom Process Variation in Deep-Submicron CMOS -- Electronic Noise in Deep-Submicron CMOS -- Temperature Effects in Deep-Submicron CMOS -- Circuit Solutions -- Conclusions and Recommendations.
000696248 506__ $$aAccess limited to authorized users.
000696248 520__ $$aOne of the most notable features of nanometer scale CMOS technology is the increasing magnitude of variability of the key device parameters affecting performance of integrated circuits. The growth of variability can be attributed to multiple factors, including the difficulty of manufacturing control, the emergence of new systematic variation-generating mechanisms, and most importantly, the increase in atomic-scale randomness, where device operation must be described as a stochastic process. In addition to wide-sense stationary stochastic device variability and temperature variation, existence of non-stationary stochastic electrical noise associated with fundamental processes in integrated-circuit devices represents an elementary limit on the performance of electronic circuits. In an attempt to address these issues, Stochastic Process Variation in Deep-Submicron CMOS: Circuits and Algorithms offers unique combination of mathematical treatment of random process variation, electrical noise and temperature and necessary circuit realizations for on-chip monitoring and performance calibration. The associated problems are addressed at various abstraction levels, i.e. circuit level, architecture level and system level. It therefore provides a broad view on the various solutions that have to be used and their possible combination in very effective complementary techniques for both analog/mixed-signal and digital circuits. The feasibility of the described algorithms and built-in circuitry has been verified by measurements from the silicon prototypes fabricated in standard 90 nm and 65 nm CMOS technology.
000696248 588__ $$aDescription based on online resource; title from PDF title page (SpringerLink, viewed November 18, 2013).
000696248 650_0 $$aMetal oxide semiconductors, Complementary$$xMathematical models.
000696248 650_0 $$aStochastic processes.
000696248 830_0 $$aSpringer series in advanced microelectronics ;$$vv.48.$$x1437-0387
000696248 85280 $$bebk$$hSpringerLink
000696248 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-94-007-7781-1$$zOnline Access
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000696248 980__ $$aEBOOK
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