Nanowire-based frequency-selective capacitive photodetector for resonant detection of infrared radiation at room temperature

Characteristics of a capacitive infrared photodetector that works at room temperature by registering a change in capacitance upon illumination are reported. If used in an ideal resonant inductor-resistor-capacitor circuit, it can exhibit zero dark current, zero standby power dissipation, infinite detectivity, and infinite light-to-dark contrast ratio. It is also made frequency-selective by employing semiconductor nanowires that selectively absorb photons of energies close to the nanowire\u27s bandgap. Based on measured parameters, the normalized detectivity is estimated to be ∼3 × 107 Jones for 1.6 μm IR wavelength at room temperature

COHERENT SPIN TRANSPORT IN NANOWIRE SPIN VALVES AND NOVEL SPINTRONIC DEVICE POSSIBILITIES

We have proposed a spintronic infrared photodetector backed by experimental evidence and matched with theoretical prediction obtained in our labs. Unlike conventional photodetectors, it can work at room temperature with ideally infinite light-to-dark contrast ratio, infinite detectivity and zero dark current. The proposed idea is based on smart implementation of spin polarized transport. Electrons while travelling through one-dimensional channel show long spin relaxation length if they can be confined to a single conduction subband because of the elimination of major spin relaxation mechanism, namely the D’yakonov-Perel’ mechanism. With infrared light, electrons can be excited to higher subbands, resulting in the revival of DP mechanism which shortens the spin relaxation length. A noticeable change in current in a nanowire spin-valve (a semiconductor nanowire with two ferromagnetic contacts) can be observed due to this shortening and this phenomenon can be manipulated to implement infrared photo-detection. An array of tri-layer nanowires have been fabricated using electrodeposition where a narrow band semiconductor InSb has been sandwiched between two ferromagnetic contacts, Cobalt and Nickel. The two magnetic contacts act as spin injector and detector, where in the InSb layer, spin polarization is modulated using infrared light. The spin-valve effect and the Hanle effect have been demonstrated in these structures, which gives the confidence that the proposed device is indeed capable of injecting, coherently transporting and detecting spin of the electrons at room temperature even in the presence of thermal drift, background magnetoresistance, low spin injection and detection efficiency. When the same experiment was done under the infrared light, spin-valve effect was still there but muted, which means, infrared light is responsible weakening the spin polarization of carriers in the InSb layer. With choice of other materials, which show better spin injection and detection efficiency, the detectivity and sensitivity can be made more prominent.https://scholarscompass.vcu.edu/gradposters/1008/thumbnail.jp

Motional modes in bulk powder and few-molecule clusters of tris(8-hydroxyquinoline aluminum) and their relation to spin dephasing

The ensemble averaged spin dephasing rate of localized electrons in the organic molecule tris(8-hydroxyquinoline aluminum) or Alq3 has been found to be significantly larger in bulk powder than in single- or few-molecule clusters confined within 1–2 nm sized nanocavities [B. Kanchibotla et al., Phys. Rev. B78, 193306 (2008)]. To understand this observation, we have compared the midinfrared absorption spectra of bulk powder and single- or few-molecule clusters. It appears that molecules have additional vibrational modes in bulk powder possibly due to multimerization. Their coupling with spin may be responsible for the increased spin dephasing rate in bulk powder

A Self-Similar Sine-Cosine Fractal Architecture for Multiport Interferometers

Multiport interferometers based on integrated beamsplitter meshes have recently captured interest as a platform for many emerging technologies. In this paper, we present a novel architecture for multiport interferometers based on the Sine-Cosine fractal decomposition of a unitary matrix. Our architecture is unique in that it is self-similar, enabling the construction of modular multi-chiplet devices. Due to this modularity, our design enjoys improved resilience to hardware imperfections as compared to conventional multiport interferometers. Additionally, the structure of our circuit enables systematic truncation, which is key in reducing the hardware footprint of the chip as well as compute time in training optical neural networks, while maintaining full connectivity. Numerical simulations show that truncation of these meshes gives robust performance even under large fabrication errors. This design is a step forward in the construction of large-scale programmable photonics, removing a major hurdle in scaling up to practical machine learning and quantum computing applications.Comment: 8 pages, 5 figure

Single chip photonic deep neural network with accelerated training

As deep neural networks (DNNs) revolutionize machine learning, energy consumption and throughput are emerging as fundamental limitations of CMOS electronics. This has motivated a search for new hardware architectures optimized for artificial intelligence, such as electronic systolic arrays, memristor crossbar arrays, and optical accelerators. Optical systems can perform linear matrix operations at exceptionally high rate and efficiency, motivating recent demonstrations of low latency linear algebra and optical energy consumption below a photon per multiply-accumulate operation. However, demonstrating systems that co-integrate both linear and nonlinear processing units in a single chip remains a central challenge. Here we introduce such a system in a scalable photonic integrated circuit (PIC), enabled by several key advances: (i) high-bandwidth and low-power programmable nonlinear optical function units (NOFUs); (ii) coherent matrix multiplication units (CMXUs); and (iii) in situ training with optical acceleration. We experimentally demonstrate this fully-integrated coherent optical neural network (FICONN) architecture for a 3-layer DNN comprising 12 NOFUs and three CMXUs operating in the telecom C-band. Using in situ training on a vowel classification task, the FICONN achieves 92.7% accuracy on a test set, which is identical to the accuracy obtained on a digital computer with the same number of weights. This work lends experimental evidence to theoretical proposals for in situ training, unlocking orders of magnitude improvements in the throughput of training data. Moreover, the FICONN opens the path to inference at nanosecond latency and femtojoule per operation energy efficiency.Comment: 21 pages, 10 figures. Comments welcom

Metal-Optic Nanophotonic Modulators in Standard CMOS Technology

Integrating nanophotonics with electronics promises revolutionary applications, from LiDAR to holographic displays. Although silicon photonics is maturing, realizing active nanophotonics in the ubiquitous bulk CMOS processes remains challenging. We introduce a fabless approach to embed active nanophotonics in bulk CMOS by co-designing the back-end-of-line metal layers for optical functionality. Using a 65nm CMOS process, we create plasmonic liquid crystal modulators with switching speeds 100x faster than commercial technologies. This zero-change nanophotonics method could equip mass-produced chips with optical communications, sensing and imaging. Embedding nanophotonics in the dominant electronics platform democratizes nanofabrication, spawning technologies from chip-scale LiDAR to holographic light-field displays

Frequency down-conversion for quantum networking with nitrogen-vacancy centers in diamond

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 49-54).Quantum frequency conversion (QFC) devices are critical to building long-distance quantum networks, which would connect quantum memories located at distant nodes through optical channels for efficient entanglement distribution. The nitrogen-vacancy (NV) center in diamond is an attractive candidate for these memories because of its long coherence time and the ability to optically write to and read out information from its spin. However, the NV-center fluoresces in the visible range, which experiences strong losses (8 dB/km) in optical fiber and has limited the current distance record for entanglement between two NVs to 1.3 km. Using difference frequency generation, we demonstrate a free-space quantum frequency conversion system that could be used to convert photons emitted by the NV to 1080 nm. This thesis reports the building and characterization of the system, which demonstrates exceptionally high signal-to-noise ratio (SNR). While not as optimal as conversion to the telecom C-band, losses at 1080 nm are significantly lower (<2 dB/km), and along with the system's high SNR, should enable much longer distance entanglement experiments than previously achieved.by Saumil Bandyopadhyay.M. Eng

Hardware error correction for programmable photonics

Programmable photonic circuits of reconfigurable interferometers can be used to implement arbitrary operations on optical modes, facilitating a flexible platform for accelerating tasks in quantum simulation, signal processing, and artificial intelligence. A major obstacle to scaling up these systems is static fabrication error, where small component errors within each device accrue to produce significant errors within the circuit computation. Mitigating this error usually requires numerical optimization dependent on real-time feedback from the circuit, which can greatly limit the scalability of the hardware. Here we present a deterministic approach to correcting circuit errors by locally correcting hardware errors within individual optical gates. We apply our approach to simulations of large scale optical neural networks and infinite impulse response filters implemented in programmable photonics, finding that they remain resilient to component error well beyond modern day process tolerances. Our results highlight a new avenue for scaling up programmable photonics to hundreds of modes within current day fabrication processes.Comment: 23 pages (9 main and 14 supplementary), 13 figures (6 main and 7 supplementary). Comments welcom