Fast Eye Diagram Analysis for High-Speed CMOS Circuits

Ahmadyan, Seyed Nematollah; Gu, Chenjie; Natarajan, Suriyaprakash; Chiprout, Eli; Vasudevan, Shobha

doi:10.7873/date.2015.0463

Cited by 8 publications

(1 citation statement)

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“…This involves the simulation of a large number of transmitted data bits and the evaluation of their electrical characteristics at each sampling point in the time domain. The segmented and overlapped representation of this time domain data is known as eye diagram (Gao et al, 2010;Ahmadyan et al, 2015).…”

Section: Applicationmentioning

confidence: 99%

Application Of Genetic Optimization Algorithms To Lumped Circuit Modelling Of Coupled Planar Coils

Werner¹,

Nolle²,

Schuett³

2017

ECMS 2017 Proceedings Edited by Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, Ján

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In portable electronic devices, like smart phones, coupled planar coils are often used as common mode filters (CMF). The purpose of these CMF is to suppress electromagnetic interference (EMI) between wireless communications systems (e.g. WIFI) and digital highspeed interfaces (e.g. USB 3). A designer of such an electronic device usually carries out a signal integrity (SI) analysis, using models of the system components. There are two alternative ways of modelling the CMF: One is based on matrices (called S-parameters) that describe the behaviour in the frequency domain and are either derived from measurements or simulation tools. The other is using a representation based on lumped circuit networks. In this work, a lumped network is generated manually based on expert knowledge. The advantage of this approach is the reduced number of only passive network components compared to traditional methods that produce much larger networks comprising of many active and passive devices. On the other hand, suitable component values of the lumped network need to be found so that the network exhibits the same frequency response as the physical device. Since there are many interacting parameters to be tuned, this cannot be achieved manually. Hence, a genetic algorithm is applied to this optimisation problem. Two sets of experiments were carried out and a sensitivity analysis has been conducted. It has been shown that the proposed method is capable of finding near optimal solutions within reasonable computation time.

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Section: Applicationmentioning

confidence: 99%