Adaptive Photochemical Nonlinearities for Optical Neural Networks

Optical neural networks (ONNs) hold great potential for faster and more energy-efficient information processing in coherent photonic circuits. To realize ONNs, linear combinations and nonlinear activation functions have to be implemented in an optical fashion. Optical nonlinearities are, however, still difficult to achieve, and existing designs are usually too inflexible to offer different activation functions as used in artificial neural networks. Herein, the nonlinear properties of the large and highly adaptive class of photoswitchable chemical compounds is made accessible as activation functions in ONNs by employing photo-induced isomerization in azobenzenes to steer activation behavior through nonlinear modulation of an information-carrying optical signal. The strength of the nonlinearity can be controlled by the chemical concentration while a physically motivated model describes the experimental data for systematically varied photoswitching parameters, resulting in a tunable yet interpretable activation function. Employing such an activation function in a neural network then allows to gauge its strength and perform established classification tasks. The work combines recent advances with photoswitchable chemical compounds and optical neural networks to enable control over the design of nonlinear activation functions, thus opening exciting perspectives for explaining the emergence of intelligent behavior in neural networks.