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Russell L. T. Schwartz

Publications and source records attributed to Russell L. T. Schwartz.

3 recordsLinked to original sources

MoS2 Based 2D Material Photodetector Array with high Pixel Density

Arrays of photodetector-based pixel sensors are ubiquitous in modern devices, such as smart phone cameras, automobiles, drones, laptops etc. Two dimensional (2D) material-based photodetector arrays are a relevant candidate, especially for applications demanding planar formfactors. However, shortcomings in pixel density and prototyping without cross contamination limit technology adoption and impact. Also, while 2D material detectors offer high absorption, graphene's closed bandgap results in undesirably high dark currents. Here, we introduce the experimental demonstration of dense planar photodetector arrays. We demonstrate a micrometer narrow pitched 2D detector pixels and show this approach's repeatability by verifying performance of a 16-pixel array. Such dense and repeatable detector realization is enabled by a novel, selective, contamination free 2D material transfer system, that we report here in automated operation. The so realized photodetectors responsivity peaks at 0.8 A/W. Furthermore, we achieve uniform detector performance via bias voltage tuning calibration to maximize deployment. Finally, we demonstrate 2D arrayed photodetectors not only on a silicon chip platform but also demonstrate and very array performance on flexible polymer substrates. Densely arrayed, flat, bendable, and uniform performing photodetector pixels enable emerging technologies in the space where lightweight and reliable performance is required, such as for smart phones and emerging AR/VR markets, but smart gadgets, wearables, and for SWAP constrained aviation and space platforms.

physics.optics↗

Near-energy-free Photonic Fourier Transformation for Convolution Operation Acceler

Convolutional operations are computationally intensive in artificial intelligence services, and their overhead in electronic hardware limits machine learning scaling. Here, we introduce a photonic joint transform correlator (pJTC) using a near-energy-free on-chip Fourier transformation to accelerate convolution operations. The pJTC reduces computational complexity for both convolution and cross-correlation from O(N4) to O(N2), where N2 is the input data size. Demonstrating functional Fourier transforms and convolution, this pJTC achieves 98.0% accuracy on an exemplary MNIST inference task. Furthermore, a wavelength-multiplexed pJTC architecture shows potential for high throughput and energy efficiency, reaching 305 TOPS/W and 40.2 TOPS/mm2, based on currently available foundry processes. An efficient, compact, and low-latency convolution accelerator promises to advance next-generation AI capabilities across edge demands, high-performance computing, and cloud services.

physics.optics↗

High Throughput Multi-Channel Parallelized Diffraction Convolutional Neural Network Accelerator

Convolutional neural networks are paramount in image and signal processing including the relevant classification and training tasks alike and constitute for the majority of machine learning compute demand today. With convolution operations being computationally intensive, next generation hardware accelerators need to offer parallelization and algorithmic-hardware homomorphism. Fortunately, diffractive display optics is capable of million-channel parallel data processing at low latency, however, thus far only showed tens of Hertz slow single image and kernel capability, thereby significantly underdelivering from its performance potential. Here, we demonstrate an operation-parallelized high-throughput Fourier optic convolutional neural network accelerator. For the first time simultaneously processing of multiple kernels in Fourier domain enabled by optical diffraction has been achieved alongside with already conventional in the field input parallelism. Additionally, we show an about one hundred times system speed up over existing optical diffraction-based processors and this demonstration rivals performance of modern electronic solutions. Therefore, this system is capable of processing large-scale matrices about ten times faster than state of art electronic systems.

cs.LG↗