Graphene Excitable Laser for Photonic Spike Processing

Graphene Excitable Laser

Princeton Docket # 14-2965

Researchers in the Department of Electrical Engineering at Princeton University have demonstrated a novel excitable laser employing passively Q-switching with a graphene-based saturable absorber (SA). Princeton is seeking an industry collaborator to commercialize this technology.

Graphene, a Nobel Prize winning material, is a 2D layer of carbon atoms arranged in a honeycomb lattice that exhibits remarkable electrical and optical properties. Unlike previous absorbers based on semiconductor materials, the laser based on the optical nonlinear saturable absorption of graphene includes the following unique features: ultrafast (picosecond) operation, low saturable absorption threshold, large modulation depth, and wavelength-independent operation with absorption of 2.3% of light per layer. In addition, the cost of fabrication is estimated to be several orders of magnitude less, i.e., less than $1 per absorber, than commercial semiconductor saturable absorbers, or SESAMs, which typically cost hundreds of dollars.

Researchers at Princeton have demonstrated that a graphene SA laser exhibits excitability near a saddle-node homoclinic bifurcation. As the phase-space excursion resulting from an excitable response is stereotyped and repeatable, the emitted laser pulses have identical pulse profiles, an important property for pulse regeneration, reshaping, and signal integrity for processing. The outputs can also be asynchronously triggered with the output phase locking to input perturbations. In addition, this system is capable of emitting spike doublets, in which the interspike timing encodes input amplitude. Doublet encoding can play an important role in selective activation of resonant subcircuits.

It is anticipated that this technology can be used in various signal-processing applications, including pulse reshaping, thresholding, 2R regeneration, and as an enabler for applications of optical computing.  Additionally, current research has demonstrated that these excitable systems could behave analogously to spiking neurons, leading to their potential use for biologically inspired cortical algorithms for learning and adaptive control.