Modeling and analysis of nonstationary low-frequency noise in circuit simulators: Enabling non Monte Carlo techniques

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

2015

Self Cite

Defects or traps in semiconductors and nano devices that randomly capture and emit charge carriers result in lowfrequency noise, such as burst and 1/f noise, which are important concerns in the design of both analog and digital circuits. The capture and emission rates of these traps are functions of the time-varying voltages across the device, resulting in nonstationary noise characteristics. Modeling of low-frequency, nonstationary noise in circuit simulators is a long-standing open problem. It has been realized that the low-frequency noise models in circuit simulators were the culprits that produced erroneous noise performance results for circuits under strongly time-varying bias conditions. In this paper, we present two fully nonstationary models for traps, a fine-grained Markov chain model and a coarsegrained Langevin model based on similar models for ion channels in neurons. The nonstationary trap models we present subsume and unify all of the work that has been done recently in the device modeling and circuit design literature on modeling nonstationary trap noise. We provide a detailed explication of these models with regard to their stochastic properties and develop carefully crafted circuit simulation techniques that are stochastically correct. We have implemented the proposed techniques in a Matlab ® based circuit simulator, by expanding the industry standard compact MOSFET model PSP to include a nonstationary description of oxide traps. We present results obtained by this extended model and the proposed simulation techniques for the low-frequency noise characterization of a common source amplifier and the phase jitter of a ring oscillator.

Section: Discussionmentioning

confidence: 96%

Modeling and Simulation of Low-Frequency Noise in Nano Devices: Stochastically Correct and Carefully Crafted Numerical Techniques

Mahmutoglu

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

2015

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“…In order for the 1 The detailed description of the general trap model and the new noise analysis algorithms are beyond the scope of this paper. The interested reader is referred to [14], [24], [26] for details. comparison with the idealized model to be meaningful, for the simulations presented in this section, the trap parameters were chosen in such a way so that the capture and emission rates become equal (with a value as in the ideal trap model) at DC steady-state when the gate voltage is fixed at the high value.…”

Section: F Limitations Of the Idealized Trap Modelmentioning

confidence: 99%

“…TRAP NOISE SIMULATIONS BASED ON A REALISTIC PHYSICS BASED MODEL In order to conduct a more detailed investigation of the case at hand, we will now use a more realistic, physics based trap model, integrated into the industry standard compact MOSFET model PSP [25]. This very general and realistic trap model (RTM) was implemented in a circuit simulator in conjunction with new noise analysis algorithms 1 that can handle nonstationary trap dynamics in the most general setting [14], [24]- [26]. We next present results obtained by the non-ideal, realistic trap model and the noise analysis techniques mentioned above.…”

Section: F Limitations Of the Idealized Trap Modelmentioning

confidence: 99%

“…Trap variables are then described by stochastic differential equations which lend themselves to be used in semi-analytical non Monte Carlo simulation techniques. Such a method, based on harmonic balance analysis, was used in [26] in order to perform noise analysis on circuits with periodic excitations. This method can be applied to the switched circuit setup treated in this paper and, as such, internal trap dynamics in the circuit can be computed and observed.…”

Section: F Limitations Of the Idealized Trap Modelmentioning

confidence: 99%

“…In the noise analysis algorithm (NAA) described in [26], trap variables (trap states) are included among the circuit variables in addition to node voltages and other current variables. Trap variables are then described by stochastic differential equations which lend themselves to be used in semi-analytical non Monte Carlo simulation techniques.…”

Section: F Limitations Of the Idealized Trap Modelmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of Low-Frequency Noise in Switched MOSFET Circuits: Revisited and Clarified

Mahmutoglu

IEEE Trans. Circuits Syst. I

2015

Self Cite

Traps that are located in the gate oxide of MOSFETs have been established as a cause of low-frequency noise phenomena. Analysis of such noise is usually based on frequency domain, stationary models. It has been shown that such simplistic models produce erroneous results for circuits with time-varying bias conditions. Tian et al. proposed an idealized trap model with the goal of capturing the nonstationary behavior of oxide traps, and were able to elucidate the experimentally observed large noise power reduction in switched MOSFET circuits which eluded any explanation obtainable with legacy stationary models. In this paper, we build on their seminal work and first identify an oversight in their model derivation which had produced an incorrect expression for the single trap noise spectrum. We next derive the correct spectrum expression, verify it against detailed idealized trap simulations and discuss its implications. The idealized trap model is amenable to analytical derivations and useful as a first stage in understanding nonstationary trap noise. We then demonstrate that noise simulations based on a detailed trap description implemented in a compact MOSFET model in a circuit simulator are needed for an accurate characterization of low-frequency noise in switched MOSFET circuits that matches experimental results.Index Terms-Low-frequency noise, noise analysis, nonstationary noise, RTS noise.

Spike timing precision of neuronal circuits

Kilinc

2018

J Comput Neurosci

Spike timing is believed to be a key factor in sensory information encoding and computations performed by the neurons and neuronal circuits. However, the considerable noise and variability, arising from the inherently stochastic mechanisms that exist in the neurons and the synapses, degrade spike timing precision. Computational modeling can help decipher the mechanisms utilized by the neuronal circuits in order to regulate timing precision. In this paper, we utilize semi-analytical techniques, which were adapted from previously developed methods for electronic circuits, for the stochastic characterization of neuronal circuits. These techniques, which are orders of magnitude faster than traditional Monte Carlo type simulations, can be used to directly compute the spike timing jitter variance, power spectral densities, correlation functions, and other stochastic characterizations of neuronal circuit operation. We consider three distinct neuronal circuit motifs: Feedback inhibition, synaptic integration, and synaptic coupling. First, we show that both the spike timing precision and the energy efficiency of a spiking neuron are improved with feedback inhibition. We unveil the underlying mechanism through which this is achieved. Then, we demonstrate that a neuron can improve on the timing precision of its synaptic inputs, coming from multiple sources, via synaptic integration: The phase of the output spikes of the integrator neuron has the same variance as that of the sample average of the phases of its inputs. Finally, we reveal that weak synaptic coupling among neurons, in a fully connected network, enables them to behave like a single neuron with a larger membrane area, resulting in an improvement in the timing precision through cooperation.