Machine learning assisted abstraction of photonic integrated circuits in fully disaggregated transparent optical networks

Author(s):  
Ihtesham Khan ◽  
Maryvonne Chalony ◽  
Enrico Ghillino ◽  
M Umar Masood ◽  
Jigesh Patel ◽  
...  
Author(s):  
Steve Grubb ◽  
Radha Nagarajan ◽  
Masaki Kato ◽  
Fred Kish ◽  
Dave Welch

2020 ◽  
Vol 10 (11) ◽  
pp. 4024
Author(s):  
Cátia Pinho ◽  
Francisco Rodrigues ◽  
Ana Maia Tavares ◽  
Carla Rodrigues ◽  
Cláudio Emanuel Rodrigues ◽  
...  

The development of photonic integrated circuits (PIC) for access network applications, such as passive optical networks (PON), constitutes a very attractive ecosystem due to PON’s potential mass market. The implementation of PIC solutions in this context is expected to facilitate the possibility of increasing the complexity and functionalities of devices at a potentially lower cost. We present a review addressing the prominent access network market requirements and the main restrictions stemming from its specific field of application. Higher focus is given to PON devices for the optical network unit (ONU) and the implications of designing a device ready for market by discussing its various perspectives in terms of technology and cost. The discussed PIC solutions/approaches in this paper are mainly based on indium phosphide (InP) technology, due to its monolithic integration capabilities. A comprehensive set of guidelines considering the current technology limitations, benefits, and processes are presented. Additionally, key current approaches and efforts are analyzed for PON next generations, such as next-generation PON 2 (NGPON2) and high-speed PON (HSP).


Author(s):  
Navid Asadizanjani ◽  
Sachin Gattigowda ◽  
Mark Tehranipoor ◽  
Domenic Forte ◽  
Nathan Dunn

Abstract Counterfeiting is an increasing concern for businesses and governments as greater numbers of counterfeit integrated circuits (IC) infiltrate the global market. There is an ongoing effort in experimental and national labs inside the United States to detect and prevent such counterfeits in the most efficient time period. However, there is still a missing piece to automatically detect and properly keep record of detected counterfeit ICs. Here, we introduce a web application database that allows users to share previous examples of counterfeits through an online database and to obtain statistics regarding the prevalence of known defects. We also investigate automated techniques based on image processing and machine learning to detect different physical defects and to determine whether or not an IC is counterfeit.


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