Recent advances in neuromorphic computing have established a computational framework that removes the processor-memory bottleneck evident in traditional von Neumann computing. Moreover, contemporary photonic circuits have addressed the limitations of electrical computational platforms to offer energy-efficient and parallel interconnects independently of the distance. When employed as synaptic interconnects with reconfigurable photonic elements, they can offer an analog platform capable of arbitrary linear matrix operations, including multiply–accumulate operation and convolution at extremely high speed and energy efficiency. Both all-optical and optoelectronic nonlinear transfer functions have been investigated for realizing neurons with photonic signals. A number of research efforts have reported orders of magnitude improvements estimated for computational throughput and energy efficiency. Compared to biological neural systems, achieving high scalability and density is challenging for such photonic neuromorphic systems. Recently developed tensor-train-decomposition methods and three-dimensional photonic integration technologies can potentially address both algorithmic and architectural scalability. This tutorial covers architectures, technologies, learning algorithms, and benchmarking for photonic and optoelectronic neuromorphic computers.

The scanning electron microscope provides a platform for subnanometer resolution characterization of material morphology with excellent topographic and chemical contrast dependent on the used detectors. For imaging applications, the predominantly utilized signals are secondary electrons (SEs) and backscattered electrons (BSEs) that are emitted from the sample surface. Recent advances in detector technology beyond the traditional Everhart-Thornley geometry have enabled the simultaneous acquisition and discrimination of SE and BSE signals. This study demonstrates the imaging capabilities of a recently introduced new detector system that consists of the combination of two in-lens (I-L) detectors and one in-column (I-C) detector. Coupled with biasing the sample stage to reduce electron-specimen interaction volumes, this trinity of detector geometry allows simultaneous acquisition of signals to distinguish chemical contrast from topographical changes of the sample, including the identification of surface contamination. The I-C detector provides 4× improved topography, whereas the I-L detector closest to the sample offers excellent simultaneous chemical contrast imaging while not limiting the minimization of working distance to obtain optimal lateral resolution. Imaging capabilities and contrast mechanisms for all three detectors are discussed quantitatively in direct comparison to each other and the conventional Everhart-Thornley detector.

We design, fabricate and characterize a 4-layer 110mm×44mm Si3N4 PIC with long routing waveguides and arrayed waveguides gratings based on a wafer-scale integration process for a high-resolution interferometric imager with 1200nm~1600nm bandwidth.