Realization of Optical DEMUX using Machine Learning Model: A Future Paradigm for Optical-VLSI System

ABSTRACT

Machine learning using the ANN approach is presented in this paper to design 101 channels of optical DEMUX in the range of 1300–1400 nm. This work is used to envisage the optical parameters of silicon grating structure such as the thickness of SiO and Si which are the decisive factors to allow the desired signal through the structure to realize optical DEMUX. This is accomplished by training an ANN model with a few pre-defined optical DEMUX parameters which are obtained beforehand by the plane wave expansion method. Here, the ANN model is implemented in the Tensor Flow framework of Google Colaboratory Network (Colab) which consists of five hidden layers of ReLU activation besides the input and output linear activation nodes. Besides this, the optical parameters collected from the ANN model are used to simulate through the PWE method for validation by observing the reflectance characteristics curve and found that most of the values are satisfied to realize the optical VLSI DEMUX. The outcomes of the present work could be beneficial in the field of optical VLSI systems pertaining to the optical interconnect.

KEYWORDS:

• Photonic crystal filter
• PWE simulation
• ANN
• Google Colab
• Tensor Flow

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Suryakanta Nayak

S K Nayak received his MCA degree from BPUT, Rourkela, India in 2009, MTech degree in computer science engineering from BPUT, Rourkela, India in 2013, Bhubaneswar, India, and a PhD degree from Utkal University, Odisha in 2021. His areas of interest are optical electronics, machine learning, IoT, etc.

Email: [email protected]

K. P. Swain

K P Swain received his BTech degree in electronics and communication engineering from BPUT, Rourkela, India, in 2003, MTech degree in electronics and communication engineering from KIIT, Bhubaneswar, India, in 2007 and PhD degree from BPUT, Rourkela, Odisha, in 2021. His areas of interest are optical electronics, embedded systems, machine learning, IoT, etc.Email: [email protected]

Rajesh Arunachalam

A Rajesh received his BE degree in ECE 2007 from Anna University, Tamil Nadu, India; MTech in embedded system technology in 2012 from SRM University, Kattankulathur, Tamil Nadu, India, and PhD in image processing from Shri Jagadish Prasad Jhabharmal Tibrewala University, Rajasthan in 2017. His research interests are wireless sensor networks, internet of things, image processing, fiber optic networks.Email: [email protected]

M. Panda

M Panda received his B Engg degree in electronics and communication engineering from Utkal University, Odisha in 1997, M Engg degree from VSSUT, Burla, India in 2002 and a PhD degree from Berhampur University, Odisha in 2010. His areas of interest are data mining, machine learning, IoT intrusion detection, social networking, wireless sensor networks, image processing, etc.Email: [email protected]

S. K. Sahu

Sanjay Sahu received his BTech degree in ECE, 2003 from BPUT, Odisha, India, MTech degree in electronics and communication engineering from Anna University in 2006, and a PhD degree from KIIT deemed to be University Bhubaneswar, Odisha in 2017 India. His research includes the field of photonics and optoelectronics.Email: [email protected]

G. Palai

G Palai received his BTech degree in applied electronics from Utkal University in 1997, MTech degree in AST from IIT Kharagpur, India in 2001 and a PhD degree from BU, Berhampur, Odisha in 2013. His areas of interest are optical electronics, machine learning, IoT, etc.