Integrated dielectric scatterers for fast optical classification of biological cells

Alessio Lugnan; Joni Dambre; Peter Bienstman

doi:10.1117/12.2306654

21 May 2018 Integrated dielectric scatterers for fast optical classification of biological cells

Alessio Lugnan, Joni Dambre, Peter Bienstman

Proceedings Volume 10689, Neuro-inspired Photonic Computing; 1068907 (2018) https://doi.org/10.1117/12.2306654
Event: SPIE Photonics Europe, 2018, Strasbourg, France

Abstract

The development of label-free, high-speed, automated and integrated cell sorting solutions is of particular interest for several biomedical applications. The employment of digital holographic microscopy in microfluidic flow cytometry gives access to a large amount of information regarding the 3D refractive index structure of a cell. In the presented work a passive, linear, integrated photonic stage is proposed as an effective nonlinear mixing interface between the hologram projection and the image sensor, allowing for a fast, compact and power-efficient extreme learning machine (ELM) implementation. The required nonlinearity comes from the sinusoid-based transfer function between the phase-shift accumulated by the light through the cell and the field intensity measured by the detector. 2D FDTD simulations with 2 classes of randomized cell models (normal and cancer cells differing in their average nucleus size) have been employed to train and test a readout linear classifier. A collection of silicon nitride pillar scatterers embedded in a silica cladding are interposed between the cell and the intensity monitor, in order to increase the complexity of the acquired interference pattern and to assist the readout linear classifier. The results show that, employing green light, the presence of scatterer layers decreases the classification error rate up to ~ 50% with respect to the case without scatterers. Such improvement can be further increased to a factor ~ 5 when a properly designed integrated optical cavity containing the cell is considered. An intuitive argumentation that explains these results is provided.

Citation Download Citation

Alessio Lugnan, Joni Dambre, and Peter Bienstman "Integrated dielectric scatterers for fast optical classification of biological cells", Proc. SPIE 10689, Neuro-inspired Photonic Computing, 1068907 (21 May 2018); https://doi.org/10.1117/12.2306654

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available