A CMOS Multi-Modality Million-Well Culture Plate with In-Situ Machine Learning Computation for High-Throughput Quantitative Exoelectrogen Screening

Abstract

We propose a CMOS million-well plate for exoelectrogen screening, supporting sub-pA current detection, multi-modal sensing, 3D-printed micro-wells, electrical actuation, and machine learning. The platform potentially supports real-time screening of an exoelectrogen library with 108 variants, achieving 106 throughput improvement.

Document Details

Document Type
DoD Grant Award
Publication Date
May 05, 2017
Source ID
N000141712457

Entities

People

  • Hua Wang

Organizations

  • Georgia Tech Research Corporation
  • Office of Naval Research
  • United States Navy

Tags

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Marine Propulsion Engineering and Naval Architecture
  • Oncology and Biomarker-Based Cancer Detection.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks