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We present a novel architecture for the design of single-photon detecting arrays that captures relative intensity or timing information from a scene, rather than absolute. The proposed method for capturing relative information between pixels or groups of pixels requires very little circuitry, and thus allows for a significantly higher pixel packing factor than is possible with per-pixel TDC approaches. The inherently compressive nature of the differential measurements also reduces data throughput and lends itself to physical implementations of compressed sensing, such as Haar wavelets. We demonstrate this technique for HDR imaging and LiDAR, and describe possible future applications.

 

This paper presents a novel technique for suppression of in-band artifacts from out-of-band (OOB) interference in widely tunable RF receivers. The technique employs a multi-tap inductor-capacitor network (LCN) to generate diversity in gain and phase between taps across the targeted frequency range. Using this network to feed a bank of identical receivers sharing a single local oscillator (LO) allows multiple kinds of interferer artifact to be suppressed. Here we considered spur-induced and phase noise-induced artifacts. In each case, the resulting artifacts are linearly separable from signal when the outputs of the sub-receivers are recombined. AC and transient simulations were first performed to show feasibility of the proposed approach. A prototype was implemented in 45nm CMOS which confirmed the validity of the synthetic diversity (SD) approach for suppressing interferer artifacts, showing a maximum lowering in EVM and BER of 38% and 60% respectively. 

Microscopic robots controlled by onboard integrated circuits that walk when powered by light are realized. 

A general transfer method is presented for the heterogeneous integration of different photonic and electronic materials systems and devices into a single substrate. Called BLAST, for Bond, Lift, Align, and Slide Transfer, the process works at wafer scale and offers precision alignment, high yield, varying topographies, and suitability for subsequent lithographic processing. BLAST's capabilities is demonstrated by integrating both GaAs and GaN µLEDs with silicon photovoltaics to fabricate optical wireless integrated circuits that up‐convert photons from the red to the blue. The study also shows that BLAST can be applied to a variety of other devices and substrates, including CMOS electronics, vertical cavity surface emitting lasers (VCSELs), and 2D materials. BLAST further enables the modularization of optoelectronic microsystems, where optical devices fabricated on one material substrate can be lithographically integrated with electronic devices on a different substrate in a scalable process.

 

Abstract In digital agriculture, large-scale data acquisition and analysis can improve farm management by allowing growers to constantly monitor the state of a field. Deploying large autonomous robot teams to navigate and monitor cluttered environments, however, is difficult and costly. Here, we present methods that would allow us to leverage managed colonies of honey bees equipped with miniature flight recorders to monitor orchard pollination activity. Tracking honey bee flights can inform estimates of crop pollination, allowing growers to improve yield and resource allocation. Honey bees are adept at maneuvering complex environments and collectively pool information about nectar and pollen sources through thousands of daily flights. Additionally, colonies are present in orchards before and during bloom for many crops, as growers often rent hives to ensure successful pollination. We characterize existing Angle-Sensitive Pixels (ASPs) for use in flight recorders and calculate memory and resolution trade-offs. We further integrate ASP data into a colony foraging simulator and show how large numbers of flights refine system accuracy, using methods from robotic mapping literature. Our results indicate promising potential for such agricultural monitoring, where we leverage the superiority of social insects to sense the physical world, while providing data acquisition on par with explicitly engineered systems. 

All-digital basestation (BS) architectures for millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO), which equip each radio-frequency chain with dedicated data converters, have advantages in spectral efficiency, flexibility, and baseband-processing simplicity over hybrid analog-digital solutions. For all-digital architectures to be competitive with hybrid solutions in terms of power consumption, novel signal-processing methods and baseband architectures are necessary. In this paper, we demonstrate that adapting the resolution of the analog-to-digital converters (ADCs) and spatial equalizer of an all-digital system to the communication scenario (e.g., the number of users, modulation scheme, and propagation conditions) enables orders-of-magnitude power savings for realistic mmWave channels. For example, for a 256-BS-antenna 16-user system supporting 1 GHz bandwidth, a traditional baseline architecture designed for a 64-user worst-case scenario would consume 23 W in 28 nm CMOS for the ADC array and the spatial equalizer, whereas a resolution-adaptive architecture is able to reduce the power consumption by 6.7×.