This paper presents a compact driver circuit with independent pixel-level temperature regulation for thermo-optic based 2D Nanophotonic phased arrays (NPAs) in light detection and ranging (LIDAR) and Virtual Reality (VR) applications. To minimize the interconnection density, the proposed driver unit uses only a single electrical contact to its corresponding NPA pixel for both heating and temperature measurement functions. The driver was fabricated using TSMC 65 nm technology and each unit is realized in an area of$15\ \mu\mathrm{m}\times 15\ \mu\mathrm{m}$. The design enables scalable 3D heterogeneous integration between any tile-based NPA with pixel pitch below$15\ \mu\mathrm{m}$and its electrical control system. The temperature regulation performance of the proposed circuit was characterized by intentionally introducing a ±20% variation to the load resistance to simulate the temperature deviation in the NPA. The measured phase errors are suppressed by the feedback controller to a maximum of$0.07\pi$and an average of$0.02\pi$within the full$2\pi$phase shift operation range.

The Nanophotonic Phased Array (NPA) is an emerging holographic display technology. With chip-scaled sizes, high refresh rates, and integrated light sources, a large-scale NPA can enable high-resolution real-time dynamic holographic displays. However, one of the critical challenges impeding the development of such large-scale NPAs is the high electrical power consumption required to modulate the amplitude and phase of each of the pixel elements. We argue that the modulation of all the elements on the array is, in fact, not necessary to produce a high-quality image. We propose a simple method that outputs the configuration of a sparse NPA, along with the amplitude and the phase required at each active pixel to generate the desired image at the observation plane. We identify the set of active pixels according to their optimized intensities. We observe that the brighter pixels have a greater influence on the target image, and it is these that we must focus on in image formation. Using as few as 10% of the total pixels from a dense 2D array of light-emitting elements, we show that a perceptually acceptable holographic image can be generated. We compare various sparse sampling methods through computational simulations and show that our proposed method gives superior qualitative and quantitative results. We believe our study will help advance research on sparse NPAs and facilitate the use of large-scale NPAs to display high-resolution 3D holographic images.

Holographic displays and computer-generated holography offer a unique opportunity in improving optical resolutions and depth characteristics of near-eye displays. The thermally-modulated Nanopho-tonic Phased Array (NPA), a new type of holographic display, affords several advantages, including integrated light source and higher refresh rates, over other holographic display technologies. However, the thermal phase modulation of the NPA makes it susceptible to the thermal proximity effect where heating one pixel affects the temperature of nearby pixels. Proximity effect correction (PEC) methods have been proposed for 2D Fourier holograms in the far field but not for Fresnel holograms at user-specified depths. Here we extend an existing PEC method for the NPA to Fresnel holograms with phase-only hologram optimization and validate it through computational simulations. Our method is not only effective in correcting the proximity effect for the Fresnel holograms of 2D images at desired depths but can also leverage the fast refresh rate of the NPA to display 3D scenes with time-division multiplexing.

Thermally modulated Nanophotonic Phased Arrays (NPAs) can be used as phase-only holographic displays. Compared to the holographic displays based on Liquid Crystal on Silicon Spatial Light Modulators (LCoS SLMs), NPAs have the advantage of integrated light source and high refresh rate. However, the formation of the desired wavefront requires accurate modulation of the phase which is distorted by the thermal proximity effect. This problem has been largely overlooked and existing approaches to similar problems are either slow or do not provide a good result in the setting of NPAs. We propose two new algorithms based on the iterative phase retrieval algorithm and the proximal algorithm to address this challenge. We have carried out computational simulations to compare and contrast various algorithms in terms of image quality and computational efficiency. This work is going to benefit the research on NPAs and enable the use of large-scale NPAs as holographic displays.

In this paper, we propose a combined channel segmentation and buffer insertion approach, which minimizes the number of buffers inserted while satisfying the delay constraints for routing channels of field-programmable analog arrays. A segmented routing algorithm based on minimum-cost-bipartite-matching is improved with demand awareness and used to evaluate the various routing channels generated. Experiments show that, compared to a sequential segmenting-then-buffering design, our approach can significantly reduce the total number of buffers required, while achieving improved routability and minimum average interconnect delay. It is also shown that by increasing the number of long segment appropriately, the algorithm can dramatically improve the routability with a moderate increase on the number of buffers.

The authors examine an application of analog neural networks to low-level processing of tactile sensory data. In analogy to the term early vision, the authors call the first level of processing required in tactile sensing early taction. The problem of deblurring or deconvolution of data provided by an array of tactile sensors that is also assumed to be corrupted by noise is addressed. It is noted that this inverse problem is ill posed and that the technique of regularization may be used to obtain solutions. The theory of nonlinear electrical networks is utilized to describe energy functions for a class of nonlinear networks and to show that the equilibrium states of the proposed network correspond to regularized solutions of the deblurring problem. An entropy regularizer is incorporated into the energy function of the network for the recovery of normal stress distributions. An integrated circuit prototype of the proposed network is discussed.>

The real-time solution to a basic inverse problem of tactile perception is investigated. Regularized solutions of this problem are obtained by a neural-analog network. Favorable setting times and noise immunity are observed in simulations and breadboard level experiments.>