No abstract

The development of inverse design, where computational optimization techniques are used to design devices based on certain specifications, has led to the discovery of many compact, nonintuitive structures with superior performance. Among various methods, large-scale, gradient-based optimization techniques have been one of the most important ways to design a structure containing a vast number of degrees of freedom. These techniques are made possible by the adjoint method, in which the gradient of an objective function with respect to all design degrees of freedom can be computed using only two full-field simulations. However, this approach has so far mostly been applied to linear photonic devices. Here, we present an extension of this method to modeling nonlinear devices in the frequency domain, with the nonlinear response directly included in the gradient computation. As illustrations, we use the method to devise compact photonic switches in a Kerr nonlinear material, in which low-power and high-power pulses are routed in different directions. Our technique may lead to the development of novel compact nonlinear photonic devices.

Analog machine learning hardware platforms promise to be faster and more energy-efficient than their digital counterparts. Wave physics, as found in acoustics and optics, is a natural candidate for building analog processors for time-varying signals. Here we identify a mapping between the dynamics of wave physics, and the computation in recurrent neural networks. This mapping indicates that physical wave systems can be trained to learn complex features in temporal data, using standard training techniques for neural networks. As a demonstration, we show that an inverse-designed inhomogeneous medium can perform vowel classification on raw audio signals as their waveforms scatter and propagate through it, achieving performance comparable to a standard digital implementation of a recurrent neural network. These findings pave the way for a new class of analog machine learning platforms, capable of fast and efficient processing of information in its native domain.

We introduce an electro-optic hardware platform for nonlinear activation functions in optical neural networks. The optical-to-optical nonlinearity operates by converting a small portion of the input optical signal into an analog electric signal, which is used to intensity-modulate the original optical signal with no reduction in processing speed. Our scheme allows for complete nonlinear on-off contrast in transmission at relatively low optical power thresholds and eliminates the requirement of having additional optical sources between each layer of the network. Moreover, the activation function is reconfigurable via electrical bias, allowing it to be programmed or trained to synthesize a variety of nonlinear responses. Using numerical simulations, we demonstrate that this activation function significantly improves the expressiveness of optical neural networks, allowing them to perform well on two benchmark machine learning tasks: learning a multi-input exclusive-OR (XOR) logic function and classification of images of handwritten numbers from the MNIST dataset. The addition of the nonlinear activation function improves test accuracy on the MNIST task from 85% to 94%.

Gradient-based inverse design in photonics has already achieved remarkable results in designing small-footprint, high-performance optical devices. The adjoint variable method, which allows for the efficient computation of gradients, has played a major role in this success. However, gradient-based optimization has not yet been applied to the mode-expansion methods that are the most common approaches to studying periodic optical structures such as photonic crystals. This is because, in such simulations, the adjoint variable method cannot be defined as explicitly as in standard finite-difference or finite-element time-or frequency-domain methods. Here, we overcome this gap through the use of automatic differentiation, which is a generalization of the adjoint variable method to arbitrary computational graphs. We implement the planewave expansion and the guided-mode expansion methods using an automatic differentiation library, and we show that the gradient of any simulation output can be computed efficiently and in parallel, with respect to all input parameters. We then use this implementation to optimize the dispersion of a photonic crystal waveguide, and the quality factor of an ultrasmall cavity in a lithium niobate slab. This extends photonic inverse design to an entirely new class of simulations, and more broadly highlights the importance that automatic differentiation could play in the future for tracking and optimizing complicated physical models.

We present a previously unexplored forward-mode differentiation method for Maxwell's equations, with applications in the field of sensitivity analysis. This approach yields exact gradients and is similar to the popular adjoint variable method, but provides a significant improvement in both memory and speed scaling for problems involving several output parameters, as we analyze in the context of finite-difference time-domain (FDTD) simulations. Furthermore, it provides an exact alternative to numerical derivative methods, based on finite-difference approximations. To demonstrate the usefulness of the method, we perform sensitivity analysis of two problems. First we compute how the spatial near-field intensity distribution of a scatterer changes with respect to its dielectric constant. Then, we compute how the spectral power and coupling efficiency of a surface grating coupler changes with respect to its fill factor.

No abstract

Compound semiconductors are the basis for many of the highest performance optical and electronic devices in use today. Their widespread commercial application has, however, been limited due to the high cost of substrates. Device costs can be significantly reduced if the substrate is reused in a simple, totally non‐destructive and rapid process. Here, a method that allows the indefinite reuse and recycling of wafers is demonstrated, employing a combination of epitaxial “protection layers”, plasma cleaning techniques that return the wafers to their original, pristine, and epi‐ready condition following epitaxial layer removal, and adhesive‐free bonding to a secondary plastic substrate. The generality of this process is demonstrated by fabricating high performance GaAs‐based photovoltaic cells, light emitting diodes, and metal‐semiconductor field effect transistors that are transferred without loss of performance onto flexible and lightweight plastic substrates, and then the parent wafer is recycled for subsequent growth of additional device layers. This process potentially leads to a transformational change in device cost, arising from the inevitable consumption of the wafer that accompanies conventional epitaxial liftoff followed by chemo‐mechanical polishing.