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Title: Particle swarm optimization of a rail-to-rail delay element for maximum linearity
Authors: Gauci, Jordan Lee
Gatt, Edward
Casha, Owen
De Cataldo, Giacinto
Grech, Ivan
Micallef, Joseph
Keywords: Mathematical models
Integrated circuits -- Design and construction
Genetic algorithms
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers
Citation: Gauci, J. L., Gatt, E., Casha, O., De Cataldo, G., Grech, I., & Micallef J. (2019). Particle swarm optimization of a rail-to-rail delay element for maximum linearity. 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), Paris.
Abstract: This paper illustrates the use of the Particle Swarm Optimization (PSO) algorithm to maximize the linearity of a rail-to-rail delay element. Previous approaches relied on approximating the piecewise time-delay model of the delay element through either the Newton Polynomial or the Lagrange Polynomial methods. While adequate linearity was achieved in both cases, this could be further improved. This work successfully employed the PSO algorithm to improve the linearity by reducing the mean square error such that the delay element exhibits a spurious-free dynamic range of 29.62 dB, with a delay range of 170.4 ns. The results were verified in Cadence using the X-FAB 0.18 μm technology.
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