arXiv subjects

Saumil Bandyopadhyay

Publications and source records attributed to Saumil Bandyopadhyay.

13 recordsLinked to original sources

arXiv2023

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.

physics.optics↗

arXiv2023

Telecom networking with a diamond quantum memory

Practical quantum networks require interfacing quantum memories with existing channels and systems that operate in the telecom band. Here we demonstrate low-noise, bidirectional quantum frequency conversion that enables a solid-state quantum memory to directly interface with telecom-band systems. In particular, we demonstrate conversion of visible-band single photons emitted from a silicon-vacancy (SiV) center in diamond to the telecom O-band, maintaining low noise ($g^2(0)<0.1$) and high indistinguishability ($V=89\pm8\%$). We further demonstrate the utility of this system for quantum networking by converting telecom-band time-bin pulses, sent across a lossy and noisy 50 km deployed fiber link, to the visible band and mapping their quantum states onto a diamond quantum memory with fidelity $\mathcal{F}=87\pm 2.5 \% $. These results demonstrate the viability of SiV quantum memories integrated with telecom-band systems for scalable quantum networking applications.

quant-ph↗

arXiv2022

Asymptotically Fault-Tolerant Programmable Photonics

Component errors limit the scaling of programmable coherent photonic circuits. These errors arise because the standard tunable photonic coupler -- the Mach-Zehnder interferometer (MZI) -- cannot be perfectly programmed to the cross state. Here, we introduce two modified circuit architectures that overcome this limitation: (1) a 3-splitter MZI mesh for generic errors, and (2) a broadband MZI+Crossing design for correlated errors. Because these designs allow for perfect realization of the cross state, the matrix fidelity no longer decreases with mesh size, allowing scaling to arbitrarily large meshes. The proposed architectures support progressive self-configuration, are more compact than previous MZI-doubling schemes, and do not require additional phase shifters. This eliminates a major obstacle to the development of very-large-scale linear photonic circuits.

physics.optics↗

arXiv2022

Stability of Self-Configuring Large Multiport Interferometers

Realistic multiport interferometers (beamsplitter meshes) are sensitive to component imperfections, and this sensitivity increases with size. Self-configuration techniques can be employed to correct these imperfections, but not all techniques are equal. This paper highlights the importance of algorithmic stability in self-configuration. Naive approaches based on sequentially setting matrix elements are unstable and perform poorly for large meshes, while techniques based on power ratios perform well in all cases, even in the presence of large errors. Based on this insight, we propose a self-configuration scheme for triangular meshes that requires only external detectors and works without prior knowledge of the component imperfections. This scheme extends to the rectangular mesh by adding a single array of detectors along the diagonal.

cs.ET↗

arXiv2022

Accurate Self-Configuration of Rectangular Multiport Interferometers

Multiport interferometers based on integrated beamsplitter meshes are widely used in photonic technologies. While the rectangular mesh is favored for its compactness and uniformity, its geometry resists conventional self-configuration approaches, which are essential to programming large meshes in the presence of fabrication error. Here, we present a new configuration algorithm, related to the $2\times 2$ block decomposition of a unitary matrix, that overcomes this limitation. Our proposed algorithm is robust to errors, requires no prior knowledge of the process variations, and relies only on external sources and detectors. We show that self-configuration using this technique reduces the effect of fabrication errors by the same quadratic factor observed in triangular meshes. This relaxes a significant limit to the size of multiport interferometers, removing a major roadblock to the scaling of optical quantum and machine-learning hardware.

physics.optics↗

arXiv2022

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.

physics.optics↗

arXiv2022

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.

cs.ET↗

arXiv2022

Netcast: Low-Power Edge Computing with WDM-defined Optical Neural Networks

This paper analyzes the performance and energy efficiency of Netcast, a recently proposed optical neural-network architecture designed for edge computing. Netcast performs deep neural network inference by dividing the computational task into two steps, which are split between the server and (edge) client: (1) the server employs a wavelength-multiplexed modulator array to encode the network's weights onto an optical signal in an analog time-frequency basis, and (2) the client obtains the desired matrix-vector product through modulation and time-integrated detection. The simultaneous use of wavelength multiplexing, broadband modulation, and integration detection allows large neural networks to be run at the client by effectively pushing the energy and memory requirements back to the server. The performance and energy efficiency are fundamentally limited by crosstalk and detector noise, respectively. We derive analytic expressions for these limits and perform numerical simulations to verify these bounds.

cs.ET↗

arXiv2022

Delocalized Photonic Deep Learning on the Internet's Edge

Advances in deep neural networks (DNNs) are transforming science and technology. However, the increasing computational demands of the most powerful DNNs limit deployment on low-power devices, such as smartphones and sensors -- and this trend is accelerated by the simultaneous move towards Internet-of-Things (IoT) devices. Numerous efforts are underway to lower power consumption, but a fundamental bottleneck remains due to energy consumption in matrix algebra, even for analog approaches including neuromorphic, analog memory and photonic meshes. Here we introduce and demonstrate a new approach that sharply reduces energy required for matrix algebra by doing away with weight memory access on edge devices, enabling orders of magnitude energy and latency reduction. At the core of our approach is a new concept that decentralizes the DNN for delocalized, optically accelerated matrix algebra on edge devices. Using a silicon photonic smart transceiver, we demonstrate experimentally that this scheme, termed Netcast, dramatically reduces energy consumption. We demonstrate operation in a photon-starved environment with 40 aJ/multiply of optical energy for 98.8% accurate image recognition and <1 photon/multiply using single photon detectors. Furthermore, we show realistic deployment of our system, classifying images with 3 THz of bandwidth over 86 km of deployed optical fiber in a Boston-area fiber network. Our approach enables computing on a new generation of edge devices with speeds comparable to modern digital electronics and power consumption that is orders of magnitude lower.

cs.ET↗

arXiv2021

Alignment-free photonic interconnects

Next generation optoelectronic systems will require the efficient transfer of optical signals between many discrete photonic components integrated onto a single substrate. While modern assembly processes can easily integrate thousands of electrical components onto a single board, photonic assembly is far more challenging due to the wavelength-scale alignment tolerances required. Here we address this problem by introducing an alignment-free photonic coupler robust to x, y, z displacement and angular misalignment. This alignment-free coupler engineers a translationally-invariant evanescent interaction between waveguides by intersecting them at an angle, which enables a lateral and angular alignment tolerance fundamentally larger than non-evanescent approaches such as edge coupling. We show that our approach can function as a universal photonic connector interfacing photonic integrated circuits and microchiplets across different platforms. As a potential use case, we describe a new type of self-aligning photonic circuit board enabled by our approach that greatly simplifies assembly of complex optoelectronic systems.

physics.app-ph↗

arXiv2021

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.

cs.ET↗

arXiv2015

Modulating spin relaxation in nanowires with infrared light at room temperature

Spintronic devices usually rely on long spin relaxation times and/or lengths for optimum performance. Therefore, the ability to modulate these quantities with an external agent offers unique possibilities. The dominant spin relaxation mechanism in most technologically important semiconductors is the D'yakonov-Perel' (DP) mechanism which vanishes if the spin carriers (electrons) are confined to a single conduction subband in a quantum wire grown in certain crystallographic directions, or polycrystalline quantum wires. Here, we report modulating the DP spin relaxation rate (and hence the spin relaxation length) in self assembled 50-nm diameter InSb nanowires with infrared light at room temperature. In the dark, almost all the electrons in the nanowires are in the lowest conduction subband at room temperature, resulting in near-complete absence of DP relaxation. This allows observation of spin-sensitive effects in the magnetoresistance. Under infrared illumination, electrons are photoexcited to higher subbands and the DP spin relaxation mechanism is revived, leading to a three-fold decrease in the spin relaxation length. Consequently, the spin sensitive effects are no longer observable under illumination. This phenomenon may have applications in spintronic room-temperature infrared photodetection.

cond-mat.mes-hall↗

arXiv2014

Coherent spin transport and suppression of spin relaxation in InSb nanowires with single subband occupancy at room temperature

A longstanding goal of spintronics is to inject, coherently transport, and detect spins in a semiconductor nanowire where a SINGLE quantized subband is occupied at room temperature. Here, we report achieving this goal in 50-nm diameter InSb nanowires by demonstrating both the spin-valve and the Hanle effect. The spin relaxation time in the nanowires was found to have increased by an order of magnitude over what has been reported in bulk and quantum wells due to the suppression of D'yakonov-Perel' spin relaxation as a result of single subband occupancy. These experiments raise hopes for the realization of a room-temperature Datta-Das spin transistor.

cond-mat.mes-hall↗