A non-quasi-static analysis of the transient behavior of the long-channel most valid in all regions of operation

Mancini, P.; Turchetti, Claudio; Masetti, G.

doi:10.1109/t-ed.1987.22926

Cited by 30 publications

(4 citation statements)

References 6 publications

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“…When a MOSFET is being turned on or off, its operation cannot be explained by the quasistatic models used for hand analysis and in most circuit simulators. Nonquasistatic models [44]- [47] and experimental measurements [46], [47] indicate that the transient effect when a MOSFET is switched from cutoff into saturation is a delay from the time that the gate is pulsed to the time that drain current begins to flow. Similarly, when a MOSFET is switched from saturation into cutoff, the drain current continues to flow for a short time after the transistor is switched off.…”

Section: A Synapse and Weight Storage Circuitsmentioning

confidence: 99%

Toward a general-purpose analog VLSI neural network with on-chip learning

Montalvo

Gyurcsik

Paulos

1997

IEEE Trans. Neural Netw.

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This paper describes elements necessary for a general-purpose low-cost very large scale integration (VLSI) neural network. By choosing a learning algorithm that is tolerant of analog nonidealities, the promise of high-density analog VLSI is realized. A 64-synapse, 8-neuron proof-of-concept chip is described. The synapse, which occupies only 4900 mum(2) in a 2-mum technology, includes a hybrid of nonvolatile and dynamic weight storage that provides fast and accurate learning as well as reliable long-term storage with no refreshing. The architecture is user-configurable in any one-hidden-layer topology. The user-interface is fully microprocessor compatible. Learning is accomplished with minimal external support; the user need only present inputs, targets, and a clock. Learning is fast and reliable. The chip solves four-bit parity in an average of 680 ms and is successful in about 96% of the trials.

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Section: A Synapse and Weight Storage Circuitsmentioning

confidence: 99%

Toward a general-purpose analog VLSI neural network with on-chip learning

Montalvo

Gyurcsik

Paulos

1997

IEEE Trans. Neural Netw.

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“…Closed-form solutions are difficult to obtain for the transient performance; therefore, numerical methods are commonly employed to calculate the time performance of high-speed circuits. Since in this text the analysis of the MOSFET performance will be limited to small-signal analysis, the interested reader is referred to [10], [11] for more details about large-signal analysis of MOS circuits with rapidly varying signals. …”

Section: Non Quasi-static Operation Of the Mosfetmentioning

confidence: 99%

High-Frequency Models

Galup-Montoro¹,

Schneider²

2007

International Series on Advances in Solid State Electronics and Technology

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The dynamic MOSFET model we have presented so far assumes the transistor to be in quasi-static operation, i.e., the charge densities at any position in the channel are assumed to depend on the instantaneous values of the terminal voltages only [1], [2], [3]. If, however, the rate of variation of the terminal voltages is high, the quasi-static approximation is no longer valid. In fact, the charge densities along the channel depend not only on the voltage values at a certain time but also on the history leading to the charge densities at that time [2]. The model used for fast varying signals is called the non-quasi-static model, which is the subject of this chapter.Chapter 8. High-Frequency Models Section 8.3 Non quasi-static small-signal model

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“…The channel charge then is not only a function of terminal voltages but also a function of time . It must contain time explicitly (4) The contribution due to the second term in (2) will then become 5If we relax the QS assumption, then an extra term comes in the transient response. For very fast transients, this term makes a major contribution to the terminal currents.…”

Section: Introductionmentioning

confidence: 99%

“…Several NQS models have been reported in the literature. The model described in [4] is valid for arbitrary time varying signals at the device terminals. The model involves the solution of a nonlinear partial differential equation derived using time-and position-dependent charge, current and continuity equations.…”

Section: Introductionmentioning

confidence: 99%

A new approach to model nonquasi-static (NQS) effects for mosfets-part I: large-signal analysis

Roy

Vasi

Patil

2003

IEEE Trans. Electron Devices

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This paper presents a new nonquasi-static (NQS) model for the MOSFET. The model is derived from physics and only relies on the very basic approximation needed for a charge-based model. To derive the model, a popular variational technique named Galerkin's Method has been used. The model proves to be very accurate even for extremely fast changes in the bias voltages. Simulation results show a very good match even when the rise time of the applied signal is smaller than the transit time of the device.

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A non-quasi-static analysis of the transient behavior of the long-channel most valid in all regions of operation

Cited by 30 publications

References 6 publications

Toward a general-purpose analog VLSI neural network with on-chip learning

Toward a general-purpose analog VLSI neural network with on-chip learning

High-Frequency Models

A new approach to model nonquasi-static (NQS) effects for mosfets-part I: large-signal analysis

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