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Faik Baskaya

Researcher at Boğaziçi University

Publications -  28
Citations -  363

Faik Baskaya is an academic researcher from Boğaziçi University. The author has contributed to research in topics: Field-programmable analog array & Analogue electronics. The author has an hindex of 10, co-authored 28 publications receiving 336 citations. Previous affiliations of Faik Baskaya include Georgia Institute of Technology.

Papers
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Journal ArticleDOI

A Floating-Gate-Based Field-Programmable Analog Array

TL;DR: Programming performance improved drastically by implementing the entire algorithm on-chip with an SPI digital interface and measuring results of the individual subcircuits and two system examples including an AM receiver and a speech processor are presented.
Journal ArticleDOI

Placement for large-scale floating-gate field-programable analog arrays

TL;DR: The goal in this paper is to develop the first placement algorithm for large-scale floating-gate-based FPAAs with a focus on the minimization of the parasitic effects on interconnects under various device-related constraints.
Proceedings ArticleDOI

Rapid Prototyping of Large-scale Analog Circuits With Field Programmable Analog Array

TL;DR: In this paper, a bandpass filter was used as a sample analog circuit using a large-scale field-programmable analog array (FPAA) to obtain models of the mapped circuits that can be simulated using SPICE.
Proceedings ArticleDOI

A novel yield aware multi-objective analog circuit optimization tool

TL;DR: A novel multi-objective yield aware analog sizing tool that utilizes scrambled Quasi Monte Carlo (QMC) approach for efficient yield estimation and Strength Pareto Evolutionary Algorithm-2 (SPEA2) as a search engine is proposed.
Proceedings ArticleDOI

Adaptive sized Quasi-Monte Carlo based yield aware analog circuit optimization tool

TL;DR: This paper proposes an efficient Quasi-Monte Carlo based yield aware analog circuit synthesis tool with an adaptive sampling mechanism, where a simulation budget allocation algorithm promises a more accurate yield estimation for the valuable candidates.