Computer generated optical volume elements by additive manufacturing

Abstract

<jats:title>Abstract</jats:title><jats:p>Computer generated optical volume elements have been investigated for information storage, spectral filtering, and imaging applications. Advancements in additive manufacturing (3D printing) allow the fabrication of multilayered diffractive volume elements in the micro-scale. For a micro-scale multilayer design, an optimization scheme is needed to calculate the layers. The conventional way is to optimize a stack of 2D phase distributions and implement them by translating the phase into thickness variation. Optimizing directly in 3D can improve field reconstruction accuracy. Here we propose an optimization method by inverting the intended use of Learning Tomography, which is a method to reconstruct 3D phase objects from experimental recordings of 2D projections of the 3D object. The forward model in the optimization is the beam propagation method (BPM). The iterative error reduction scheme and the multilayer structure of the BPM are similar to neural networks. Therefore, this method is referred to as Learning Tomography. Here, instead of imaging an object, we reconstruct the 3D structure that performs the desired task as defined by its input-output functionality. We present the optimization methodology, the comparison by simulation work and the experimental verification of the approach. We demonstrate an optical volume element that performs angular multiplexing of two plane waves to yield two linearly polarized fiber modes in a total volume of 128 μm by 128 μm by 170 μm.</jats:p>

Optical neural networks: The 3D connection

“…The viability of integrating photonic circuits suited for NN interconnects in 3D has recently been demonstrated in principle [3,4]. Ultimately, scalability is key for computing hardware, which implies that stacking 2D neurons and 3D interconnects into deep photonic NNs requires optical losses to be counterbalanced by amplification without resulting in an unsustainable thermal energy deposition inside the integrated photonic circuit.…”

“…We do not constrain the nature of photonic neurons or the 3D routing strategy. All-optical as well as electro-optical components acting as neurons are possible, and the 3D photonic interconnect can be realized by refractive index modifications in a 3D medium, multiple stacks of diffractive-optics planes [4] as well as complex 3D circuitry of photonic waveguides [3].…”

“…OVEs can be designed by utilizing a nonlinear optimization scheme, learning tomography (LT), which calculates the topography of either multilayered or GRIN volume elements to approximate desired mappings. Figure 5(A,B) shows an demonstration by Dinc et al, which acts as an angular multiplexer (lantern) that maps plane waves with different incidence angles to linearly polarized multimode fiber modes [4]. It provides an interconnect between single mode fibers stacked with different angles and a multimode fiber to map each single mode fiber input/output to a specific mode of multimode fiber, hence performs mode-division multiplexing.…”

Combining one and two photon polymerization for accelerated high performance (3+1)D photonic integration

“…In photonics, the DLW-TPP technique has been used to fabricate free-form and transformational components [5][6][7] , point-to-point photonic wire-bondings 8 , waveguides 9 , spatial-filters 10 , gradedindex lenses 11 and photonic components 12,13 . Simultaneously, this technique is advantageous for integrated photonic circuits 14,15 due to its ability to locally and dynamically modify optical properties on feature sizes below the Abbe resolution limit.…”

Photonics for computing and computing for photonics

“…Abdollahramezani et al [9] review the potential of metaoptics for analogue optical computing, while Stark et al [10] and Ferreira de Lima et al [11] provide overviews of the field of NNs by, respectively, focussing on potential opportunities for integrating photonic NNs and a primer on silicon neuromorphic processors. Mengu et al [12] report misalignment resilient diffractive optical networks, Dinc et al [13] demonstrate computer generated optical volume elements fabricated by additive manufacturing, while Ahmed et al [14] discuss integrated photonic Fourier transformations for optical convolutions towards efficient and high-speed NNs. Romeira et al [15] investigate nano light-emitting diodes (nano-LEDs) for energy-efficient and gigahertz-speed spikebased subwavelength neuromorphic photonic computing, Estébanez et al [16] accelerate photonic computing by bandwidth enhancement of a time-delay reservoir, while Andreoli et al [17] report their findings for Boolean learning under noise perturbations in hardware NNs.…”