Thesis (PH.D.) - Cairo University - Faculty Of Engineering - Department Of Mathematics and Physics

This work presents a novel technique for the automation of the post - fabrication circuit tuningIt introduces unsupervised learning paradigms as a new effective tool that can be employed in circuit tuningFirst , a training set that characterizes the behavior of the circuit under test is constructedThe data in this set consists of input measurement vectors with no output attributes and is clustered via unsupervised learning paradigms in order to explore its underlying structure and correlationsThe generated clusters are efficiently labeled and directly utilized in circuit tuning by calculating the value (s) of the tuning parameter (s) Moreover , a hierarchical clustering approach is suggested for a regimented manipulation of the clusters

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