Leveraging AI in Photonics and Beyond

Neuromorphic Photonics Circuits: Contemporary Review

Neuromorphic photonics is a cutting-edge fusion of neuroscience-inspired computing and photonics technology to overcome the constraints of conventional computing architectures. Its significance lies in the potential to transform information processing by mimicking the parallelism and efficiency of the human brain. Using optics and photonics principles, neuromorphic devices can execute intricate computations swiftly and with impressive energy efficiency. This innovation holds promise for advancing artificial intelligence and machine learning while addressing the limitations of traditional silicon-based computing. Neuromorphic photonics could herald a new era of computing that is more potent and draws inspiration from cognitive processes, leading to advancements in robotics, pattern recognition, and advanced data processing. This paper reviews the recent developments in neuromorphic photonic integrated circuits, applications, and current challenges.

Simulation of GHz ultrasonic wave piezoelectric instrumentation for Fourier transform computation

The recent emerging alternative to classic numerical Fast Fourier transform (FFT) computation, based on GHz ultrasonic waves generated from and detected by piezoelectric transducers for wavefront computing (WFC), is more efficient and energy-saving. In this paper, we present comprehensive studies on the modeling and simulation methods for ultrasonic WFC computation. We validate the design of the WFC system using ray-tracing, Fresnel diffraction (FD), and the full-wave finite element method (FEM). To effectively simulate the WFC system for inputs of 1-D signals and 2-D images, we verified the design parameters and focal length of an ideal plano-concave lens using the ray-tracing method. We also compared the analytical FFT solution with our Fourier transform (FT) results from 3-D and 2-D FD and novel 2-D full wave FEM simulations of a multi-level Fresnel lens with 1-D signals and 2-D images as inputs. Unlike the previously reported WFC system which catered only for 2-D images, our proposed method also can solve the 1-D FFT effectively. We validate our proposed 2-D full wave FEM simulation method by comparing our results with the theoretical FFT and Fresnel diffraction method. The FFT results from FD and FEM agree well with the digitally computed FFT, with computational complexity reduced from [Formula: see text] to O(N) for 2-D FFT, and from O(NlogN) to O(N) for 1-D FFT with a large number of signal sampling points N.

Meshless optical mode solving using scalable deep deconvolutional neural network

Optical mode solving is of paramount importance in photonic design and discovery. In this paper we propose a deep deconvolutional neural network architecture for a meshless, and resolution scalable optical mode calculations. The solution is arbitrary in wavelengths and applicable for a wide range of photonic materials and dimensions. The deconvolutional model consists of two stages: the first stage projects the photonic geometrical parameters to a vector in a higher dimensional space, and the second stage deconvolves the vector into a mode image with the help of scaling blocks. Scaling block can be added or subtracted as per desired resolution in the final mode image, and it can be effectively trained using a transfer learning approach. Being a deep learning model, it is light, portable, and capable of rapidly disseminating edge computing ready solutions. Without the loss of generality, we illustrate the method for an optical channel waveguide, and readily generalizable for wide range photonic components including photonic crystals, optical cavities and metasurfaces.

Implementation of a Fuzzy Inference System to Enhance the Measurement Range of Multilayer Interferometric Sensors

This work presents a novel methodology to implement a fuzzy inference system (FIS) to overcome the measurement ambiguity that is typically observed in interferometric sensors. This ambiguity occurs when the measurand is determined by tracing the wavelength position of a peak or dip of a spectral fringe. Consequently, the sensor measurement range is typically limited to the equivalent of 1 free spectral range (FSR). Here, it is demonstrated that by using the proposed methodology, the measurement range of this type of sensor can be widened several times by overcoming the ambiguity over some FSR periods. Furthermore, in order to support the viability of the methodology, it was applied to a couple of temperature interferometric sensors. Finally, experimental results demonstrated that it was possible to quintuple the measurement range of one of the tested sensors with a mean absolute error of MAE = 0.0045 °C, while for the second sensor, the measurement range was doubled with an MAE = 0.0073 °C.