Classification of Metamaterial-Based Defected Photonic Crystal Structure from Band-Pass Filter Characteristics Using Soft Computing Techniques

The present paper deals with the classification problem of metamaterial-based photonic crystal from its band-pass filter characteristics obtained experimentally in presence and absence of defects at optical communication spectrum of 1.55 µm. Two well-known DNG materials namely paired nanorod (n = −0.3) and nano-fishnet with elliptical void (n = −4) are considered for analysis purpose, and presence of point defects is taken into account in otherwise ideal structure which makes it as a four-class problem. Band-pass filter characteristics are measured for all the classes for both normal and oblique incidences separately with dimensional and incident angle variations; different soft computing techniques are applied for classification purpose as it is hardly possible to identify the device from the filter behavior. Apriori algorithm is utilized for association analysis to determine 100% confidence. Result shows that 98.53% accuracy is provided with neural network-based classifier with 98.93% sensitivity and 98.08% specificity when computation is made over 1000 samples.