Signal feature recognition based on lightwave neuromorphic signal processing

Mable P. Fok, Hannah Deming, Mitchell Nahmias, Nicole Rafidi, David Rosenbluth, Alexander Tait, Yue Tian, Paul R. Prucnal

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


We developed a hybrid analog/digital lightwave neuromorphic processing device that effectively performs signal feature recognition. The approach, which mimics the neurons in a crayfish responsible for the escape response mechanism, provides a fast and accurate reaction to its inputs. The analog processing portion of the device uses the integration characteristic of an electro-absorption modulator, while the digital processing portion employ optical thresholding in a highly Ge-doped nonlinear loop mirror. The device can be configured to respond to different sets of input patterns by simply varying the weights and delays of the inputs. We experimentally demonstrated the use of the proposed lightwave neuromorphic signal processing device for recognizing specific input patterns.

Original languageEnglish (US)
Pages (from-to)19-21
Number of pages3
JournalOptics Letters
Issue number1
StatePublished - Jan 1 2011

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics


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