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This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field.

The circuits comprising superconducting optoelectronic synapses, dendrites, and neurons are described by numerically cumbersome and formally opaque coupled differential equations. Reference 1 showed that a phenomenological model of superconducting loop neurons eliminates the need to solve the Josephson circuit equations that describe synapses and dendrites. The initial goal of the model was to decrease the time required for simulations, yet an additional benefit of the model was increased transparency of the underlying neural circuit operations and conceptual clarity regarding the connection of loop neurons to other physical systems. Whereas the original model simplified the treatment of the Josephson-junction dynamics, essentially by only considering low-pass versions of the dendritic outputs, the model resorted to an awkward treatment of spikes generated by semiconductor transmitter circuits that required explicitly checking for threshold crossings and distinct treatment of time steps wherein somatic threshold is reached. Here we extend that model to simplify the treatment of spikes coming from somas, again making use of the fact that in neural systems the downstream recipients of spike events almost always perform low-pass filtering. We provide comparisons between the first and second phenomenological models, quantifying the accuracy of the additional approximations. We identify regions of circuit parameter space in which the extended model works well and regions where it works poorly. For some circuit parameters it is possible to represent the downstream dendritic response to a single spike as well as coincidences or sequences of spikes, indicating the model is not simply a reduction to rate coding. The governing equations are shown to be nearly identical to those ubiquitous in the neuroscience literature for modeling leaky-integrator dendrites and neurons., Comment: 20 pages, 12 figures

The co-location of memory and processing is a core principle of neuromorphic computing. A local memory device for synaptic weight storage has long been recognized as an enabling element for large-scale, high-performance neuromorphic hardware. In this work, we demonstrate programmable superconducting synapses with integrated memories for use in superconducting optoelectronic neural systems. Superconducting nanowire single-photon detectors and Josephson junctions are combined into programmable synaptic circuits that exhibit single-photon sensitivity, memory cells with more than 400 internal states, leaky integration of input spike events, and 0.4 fJ programming energies (including cooling power). These results are attractive for implementing a variety of supervised and unsupervised learning algorithms and lay the foundation for a new hardware platform optimized for large-scale spiking network accelerators., Comment: 16 pages, 11 figures

We demonstrate superconducting single-photon detectors that integrate signals locally at each pixel. This capability is realized by the monolithic integration of superconducting-nanowire single-photon detectors with Josephson electronics. The motivation is to realize superconducting sensor elements with integrating capabilities similar to their CMOS-sensor counterparts. The pixels can operate in several modes. First, we demonstrate that photons can be counted individually, with each detection event adding an identical amount of supercurrent to an integrating element. Second, we demonstrate an active gain control option, in which the signal added per detection event can be dynamically adjusted to account for variable light conditions. Additionally, the pixels can either retain signal indefinitely to record all counts incurred over an integration period, or the pixels can record a fading signal of detection events within a decay time constant. We describe additional semiconductor readout circuitry that will be used in future work to realize scalable, large-format sensor arrays of superconducting single photon detectors compatible with CMOS array readout architectures.

Superconducting optoelectronic loop neurons are a class of circuits potentially conducive to networks for large-scale artificial cognition. These circuits employ superconducting components including single-photon detectors, Josephson junctions, and transformers to achieve neuromorphic functions. To date, all simulations of loop neurons have used first-principles circuit analysis to model the behavior of synapses, dendrites, and neurons. These circuit models are computationally inefficient and leave opaque the relationship between loop neurons and other complex systems. Here we introduce a modeling framework that captures the behavior of the relevant synaptic, dendritic, and neuronal circuits at a phenomenological level without resorting to full circuit equations. Within this compact model, each dendrite is discovered to obey a single nonlinear leaky-integrator ordinary differential equation, while a neuron is modeled as a dendrite with a thresholding element and an additional feedback mechanism for establishing a refractory period. A synapse is modeled as a single-photon detector coupled to a dendrite, where the response of the single-photon detector follows a closed-form expression. We quantify the accuracy of the phenomenological model relative to circuit simulations and find that the approach reduces computational time by a factor of ten thousand while maintaining accuracy of one part in ten thousand. We demonstrate the use of the model with several basic examples. The net increase in computational efficiency enables future simulation of large networks, while the formulation provides a connection to a large body of work in applied mathematics, computational neuroscience, and physical systems such as spin glasses., Comment: 34 total pages, 21 pages before the appendices, 31 figures, 24 figures before the appendices

Superconducting optoelectronic hardware is being explored as a path towards artificial spiking neural networks with unprecedented scales of complexity and computational ability. Such hardware combines integrated-photonic components for few-photon, light-speed communication with superconducting circuits for fast, energy-efficient computation. Monolithic integration of superconducting and photonic devices is necessary for the scaling of this technology. In the present work, superconducting-nanowire single-photon detectors are monolithically integrated with Josephson junctions for the first time, enabling the realization of superconducting optoelectronic synapses. We present circuits that perform analog weighting and temporal leaky integration of single-photon presynaptic signals. Synaptic weighting is implemented in the electronic domain so that binary, single-photon communication can be maintained. Records of recent synaptic activity are locally stored as current in superconducting loops. Dendritic and neuronal nonlinearities are implemented with a second stage of Josephson circuitry. The hardware presents great design flexibility, with demonstrated synaptic time constants spanning four orders of magnitude (hundreds of nanoseconds to milliseconds). The synapses are responsive to presynaptic spike rates exceeding 10 MHz and consume approximately 33 aJ of dynamic power per synapse event before accounting for cooling. In addition to neuromorphic hardware, these circuits introduce new avenues towards realizing large-scale single-photon-detector arrays for diverse imaging, sensing, and quantum communication applications., Comment: 23 pages, 20 figures

Superconducting electronic circuits have much to offer with regard to neuromorphic hardware. Superconducting quantum interference devices (SQUIDs) can serve as an active element to perform the thresholding operation of a neuron's soma. However, a SQUID has a response function that is periodic in the applied signal. We show theoretically that if one restricts the total input to a SQUID to maintain a monotonically increasing response, a large fraction of synapses must be active to drive a neuron to threshold. We then demonstrate that an active dendritic tree (also based on SQUIDs) can significantly reduce the fraction of synapses that must be active to drive the neuron to threshold. In this context, the inclusion of a dendritic tree provides the dual benefits of enhancing the computational abilities of each neuron and allowing the neuron to spike with sparse input activity., Comment: 8 pages, 5 figures

Any large-scale spiking neuromorphic system striving for complexity at the level of the human brain and beyond will need to be co-optimized for communication and computation. Such reasoning leads to the proposal for optoelectronic neuromorphic platforms that leverage the complementary properties of optics and electronics. Starting from the conjecture that future large-scale neuromorphic systems will utilize integrated photonics and fiber optics for communication in conjunction with analog electronics for computation, we consider two possible paths towards achieving this vision. The first is a semiconductor platform based on analog CMOS circuits and waveguide-integrated photodiodes. The second is a superconducting approach that utilizes Josephson junctions and waveguide-integrated superconducting single-photon detectors. We discuss available devices, assess scaling potential, and provide a list of key metrics and demonstrations for each platform. Both platforms hold potential, but their development will diverge in important respects. Semiconductor systems benefit from a robust fabrication ecosystem and can build on extensive progress made in purely electronic neuromorphic computing but will require III-V light source integration with electronics at an unprecedented scale, further advances in ultra-low capacitance photodiodes, and success from emerging memory technologies. Superconducting systems place near theoretically minimum burdens on light sources (a tremendous boon to one of the most speculative aspects of either platform) and provide new opportunities for integrated, high-endurance synaptic memory. However, superconducting optoelectronic systems will also contend with interfacing low-voltage electronic circuits to semiconductor light sources, the serial biasing of superconducting devices on an unprecedented scale, a less mature fabrication ecosystem, and cryogenic infrastructure., Comment: 24 pages, 10 figures

To design and construct hardware for general intelligence, we must consider principles of both neuroscience and very-large-scale integration. For large neural systems capable of general intelligence, the attributes of photonics for communication and electronics for computation are complementary and interdependent. Using light for communication enables high fan-out as well as low-latency signaling across large systems with no traffic-dependent bottlenecks. For computation, the inherent nonlinearities, high speed, and low power consumption of Josephson circuits are conducive to complex neural functions. Operation at 4\,K enables the use of single-photon detectors and silicon light sources, two features that lead to efficiency and economical scalability. Here I sketch a concept for optoelectronic hardware, beginning with synaptic circuits, continuing through wafer-scale integration, and extending to systems interconnected with fiber-optic white matter, potentially at the scale of the human brain and beyond., Comment: 10 pages, five figures, perspective article

We show several techniques for using integrated-photonic waveguide structures to simultaneously characterize multiple waveguide-integrated superconducting-nanowire detectors with a single fiber input. The first set of structures allows direct comparison of detector performance of waveguide-integrated detectors with various widths and lengths. The second type of demonstrated integrated-photonic structure allows us to achieve detection with a high dynamic range. This device allows a small number of detectors to count photons across many orders of magnitude in count rate. However, we find a stray light floor of -30 dB limits the dynamic range to three orders of magnitude. To assess the utility of the detectors for use in synapses in spiking neural systems, we measured the response with average incident photon numbers ranging from less than $10^{-3}$ to greater than $10$. The detector response is identical across this entire range, indicating that synaptic responses based on these detectors will be independent of the number of incident photons in a communication pulse. Such a binary response is ideal for communication in neural systems. We further demonstrate that the response has a linear dependence of output current pulse height on bias current with up to a factor of 1.7 tunability in pulse height. Throughout the work, we compare room-temperature measurements to cryogenic measurements. The agreement indicates room-temperature measurements can be used to determine important properties of the detectors.

We report on the fabrication and characterization of single-photon-sensitive WSi superconducting detectors with wire widths from 1 {\mu}m to 3 {\mu}m. The devices achieve saturated internal detection efficiency at 1.55 {\mu}m wavelength and exhibit maximum count rates in excess of 10^5 s^-1. We also investigate the material properties of the silicon-rich WSi films used for these devices. We find that many devices with active lengths of several hundred microns exhibit critical currents in excess of 50% of the depairing current. A meandered detector with 2.0 {\mu}m wire width is demonstrated over a surface area of 362x362 {\mu}m^2, showcasing the material and device quality achieved.

We demonstrate adiabatically tapered fibers terminating in sub-micron tips that are clad with a higher-index material for coupling to an on-chip waveguide. This cladding enables coupling to a high-index waveguide without losing light to the buried oxide. A technique to clad the tip of the tapered fiber with a higher-index polymer is introduced. Conventional tapered waveguides and forked tapered waveguide structures are investigated for coupling from the clad fiber to the on-chip waveguide. We find the forked waveguide facilitates alignment and packaging, while the conventional taper leads to higher bandwidth. The insertion loss from a fiber through a forked coupler to a sub-micron silicon nitride waveguide is 1.1 dB and the 3 dB-bandwidth is 90 nm. The coupling loss in the packaged device is 1.3 dB. With a fiber coupled to a conventional tapered waveguide, the loss is 1.4 dB with a 3 dB bandwidth extending beyond the range of the measurement apparatus, estimated to exceed 250 nm.

If the parameters defining the physics of our universe departed from their present values, the observed rich structure and complexity would not be supported. This article considers whether similar fine-tuning of parameters applies to technology. The anthropic principle is one means of explaining the observed values of the parameters. This principle constrains physical theories to allow for our existence, yet the principle does not apply to the existence of technology. Cosmological natural selection has been proposed as an alternative to anthropic reasoning. Within this framework, fine-tuning results from selection of universes capable of prolific reproduction. It was originally proposed that reproduction occurs through singularities resulting from supernovae, and subsequently argued that life may facilitate the production of the singularities that become offspring universes. Here I argue technology is necessary for production of singularities by living beings, and ask whether the physics of our universe has been selected to simultaneously enable stars, intelligent life, and technology capable of creating progeny. Specific technologies appear implausibly equipped to perform tasks necessary for production of singularities, potentially indicating fine-tuning through cosmological natural selection. These technologies include silicon electronics, superconductors, and the cryogenic infrastructure enabled by the thermodynamic properties of liquid helium. Numerical studies are proposed to determine regions of physical parameter space in which the constraints of stars, life, and technology are simultaneously satisfied. If this overlapping parameter range is small, we should be surprised that physics allows technology to exist alongside us. The tests do not call for new astrophysical or cosmological observations. Only computer simulations of well-understood condensed matter systems are required., Comment: 20 pages, two figures, seven appendices, 170 references

W centers are trigonal defects generated by self-ion implantation in silicon that exhibit photoluminescence at 1.218 $\mu$m. We have shown previously that they can be used in waveguide-integrated all-silicon light-emitting diodes (LEDs). Here we optimize the implant energy, fluence and anneal conditions to maximize the photoluminescence intensity for W centers implanted in silicon-on-insulator, a substrate suitable for waveguide-integrated devices. After optimization, we observe near two orders of magnitude improvement in photoluminescence intensity relative to the conditions with the stopping range of the implanted ions at the center of the silicon device layer. The previously demonstrated waveguide-integrated LED used implant conditions with the stopping range at the center of this layer. We further show that such light sources can be manufactured at the 300-mm scale by demonstrating photoluminescence of similar intensity from 300 mm silicon-on-insulator wafers. The luminescence uniformity across the entire wafer is within the measurement error.

The microscale integration of mid- and longwave-infrared photonics could enable the development of fieldable, robust chemical sensors, as well as highly efficient infrared frequency converters. However, such technology would be defined by the choice of material platform, which immediately determines the strength and types of optical nonlinearities available, the optical transparency window, modal confinement, and physical robustness. In this work, we demonstrate a new platform, suspended AlGaAs waveguides integrated on silicon, providing excellent performance in all of these metrics. We demonstrate low propagation losses within a span of nearly two octaves (1.26 to 4.6 $\mu$m) with exemplary performance of 0.45 dB/cm at $\lambda = 2.4$ $\mu$m. We exploit the high nonlinearity of this platform to demonstrate 1560 nm-pumped second-harmonic generation and octave-spanning supercontinuum reaching out to 2.3 $\mu$m with 3.4 pJ pump pulse energy. With mid-IR pumping, we generate supercontinuum spanning from 2.3 to 6.5 $\mu$m. Finally, we demonstrate the versatility of the platform with mid-infrared passive devices such as low-loss 10 $\mu$m-radius bends, compact power splitters with 96 $\pm$ 1% efficiency and edge couplers with 3.0 $\pm$ 0.1 dB loss. This platform has strong potential for multi-functional integrated photonic systems in the mid-IR.

Much of the information processing performed by a neuron occurs in the dendritic tree. For neural systems using light for communication, it is advantageous to convert signals to the electronic domain at synaptic terminals so dendritic computation can be performed with electrical circuits. Here we present circuits based on Josephson junctions and mutual inductors that act as dendrites, processing signals from synapses receiving single-photon communication events with superconducting detectors. We show simulations of circuits performing basic temporal filtering, logical operations, and nonlinear transfer functions. We further show how the synaptic signal from a single-photon can fan out locally in the electronic domain to enable the dendrites of the receiving neuron to process a photonic synapse event or pulse train in multiple different ways simultaneously. Such a technique makes efficient use of photons, energy, space, and information., Comment: 15 pages, 11 figures

Electrons and photons offer complementary strengths for information processing. Photons are excellent for communication, while electrons are superior for computation and memory. Cognition requires distributed computation to be communicated across the system for information integration. We present reasoning from neuroscience, network theory, and device physics supporting the conjecture that large-scale cognitive systems will benefit from electronic devices performing synaptic, dendritic, and neuronal information processing operating in conjunction with photonic communication. On the chip scale, integrated dielectric waveguides enable fan-out to thousands of connections. On the system scale, fiber and free-space optics can be employed. The largest cognitive systems will be limited by the distance light can travel during the period of a network oscillation. We calculate that optoelectronic networks the area of a large data center ($10^5$\,m$^2$) will be capable of system-wide information integration at $1$\,MHz. At frequencies of cortex-wide integration in the human brain ($4$\,Hz, theta band), optoelectronic systems could integrate information across the surface of the earth., Comment: 10 pages, 5 figures, ICRC 2018 conference paper

We design, fabricate and characterize integrated photonic routing manifolds with 10 inputs and 100 outputs using two vertically integrated planes of silicon nitride waveguides. We analyze manifolds via top-view camera imaging. This measurement technique allows the rapid acquisition of hundreds of precise transmission measurements. We demonstrate manifolds with uniform and Gaussian power distribution patterns with mean power output errors (averaged over 10 sets of 10 inputs) of 0.7 and 0.9 dB, respectively, establishing this as a viable architecture for precision light distribution on-chip. We also assess the performance of the passive photonic elements comprising the system via self-referenced test structures, including high-dynamic-range beam taps, waveguide cutback structures, and waveguide crossing arrays.

Circuits using superconducting single-photon detectors and Josephson junctions to perform signal reception, synaptic weighting, and integration are investigated. The circuits convert photon-detection events into flux quanta, the number of which is determined by the synaptic weight. The current from many synaptic connections is inductively coupled to a superconducting loop that implements the neuronal threshold operation. Designs are presented for synapses and neurons that perform integration as well as detect coincidence events for temporal coding. Both excitatory and inhibitory connections are demonstrated. It is shown that a neuron with a single integration loop can receive input from 1000 such synaptic connections, and neurons of similar design could employ many loops for dendritic processing., Comment: 14 pages, 11 figures

A superconducting optoelectronic neuron will produce a small current pulse upon reaching threshold. We present an amplifier chain that converts this small current pulse to a voltage pulse sufficient to produce light from a semiconductor diode. This light is the signal used to communicate between neurons in the network. The amplifier chain comprises a thresholding Josephson junction, a relaxation oscillator Josephson junction, a superconducting thin-film current-gated current amplifier, and a superconducting thin-film current-gated voltage amplifier. We analyze the performance of the elements in the amplifier chain in the time domain to calculate the energy consumption per photon created for several values of light-emitting diode capacitance and efficiency. The speed of the amplification sequence allows neuronal firing up to at least 20\,MHz with power density low enough to be cooled easily with standard $^4$He cryogenic systems operating at 4.2\,K., Comment: 16 pages, 11 figures

The design of neural hardware is informed by the prominence of differentiated processing and information integration in cognitive systems. The central role of communication leads to the principal assumption of the hardware platform: signals between neurons should be optical to enable fanout and communication with minimal delay. The requirement of energy efficiency leads to the utilization of superconducting detectors to receive single-photon signals. We discuss the potential of superconducting optoelectronic hardware to achieve the spatial and temporal information integration advantageous for cognitive processing, and we consider physical scaling limits based on light-speed communication. We introduce the superconducting optoelectronic neurons and networks that are the subject of the subsequent papers in this series., Comment: 10 pages, 1 figure

Networks of superconducting optoelectronic neurons are investigated for their near-term technological potential and long-term physical limitations. Networks with short average path length, high clustering coefficient, and power-law degree distribution are designed using a growth model that assigns connections between new and existing nodes based on spatial distance as well as degree of existing nodes. The network construction algorithm is scalable to arbitrary levels of network hierarchy and achieves systems with fractal spatial properties and efficient wiring. By modeling the physical size of superconducting optoelectronic neurons, we calculate the area of these networks. A system with 8100 neurons and 330,430 total synapses will fit on a 1\,cm $\times$ 1\,cm die. Systems of millions of neurons with hundreds of millions of synapses will fit on a 300\,mm wafer. For multi-wafer assemblies, communication at light speed enables a neuronal pool the size of a large data center comprising 100 trillion neurons with coherent oscillations at 1\,MHz. Assuming a power law frequency distribution, as is necessary for self-organized criticality, we calculate the power consumption of the networks. We find the use of single photons for communication and superconducting circuits for computation leads to power density low enough to be cooled by liquid $^4$He for networks of any scale., Comment: 21 pages, 16 figures

As a means of dynamically reconfiguring the synaptic weight of a superconducting optoelectronic loop neuron, a superconducting flux storage loop is inductively coupled to the synaptic current bias of the neuron. A standard flux memory cell is used to achieve a binary synapse, and loops capable of storing many flux quanta are used to enact multi-stable synapses. Circuits are designed to implement supervised learning wherein current pulses add or remove flux from the loop to strengthen or weaken the synaptic weight. Designs are presented for circuits with hundreds of intermediate synaptic weights between minimum and maximum strengths. Circuits for implementing unsupervised learning are modeled using two photons to strengthen and two photons to weaken the synaptic weight via Hebbian and anti-Hebbian learning rules, and techniques are proposed to control the learning rate. Implementation of short-term plasticity, homeostatic plasticity, and metaplasticity in loop neurons is discussed., Comment: 17 pages, 12 figures

Optical communication achieves high fanout and short delay advantageous for information integration in neural systems. Superconducting detectors enable signaling with single photons for maximal energy efficiency. We present designs of superconducting optoelectronic neurons based on superconducting single-photon detectors, Josephson junctions, semiconductor light sources, and multi-planar dielectric waveguides. These circuits achieve complex synaptic and neuronal functions with high energy efficiency, leveraging the strengths of light for communication and superconducting electronics for computation. The neurons send few-photon signals to synaptic connections. These signals communicate neuronal firing events as well as update synaptic weights. Spike-timing-dependent plasticity is implemented with a single photon triggering each step of the process. Microscale light-emitting diodes and waveguide networks enable connectivity from a neuron to thousands of synaptic connections, and the use of light for communication enables synchronization of neurons across an area limited only by the distance light can travel within the period of a network oscillation. Experimentally, each of the requisite circuit elements has been demonstrated, yet a hardware platform combining them all has not been attempted. Compared to digital logic or quantum computing, device tolerances are relaxed. For this neural application, optical sources providing incoherent pulses with 10,000 photons produced with efficiency of 10$^{-3}$ operating at 20\,MHz at 4.2\,K are sufficient to enable a massively scalable neural computing platform with connectivity comparable to the brain and thirty thousand times higher speed., Comment: 10 pages, 7 figures

We report and characterize low-temperature, plasma-deposited deuterated silicon nitride thin films for nonlinear integrated photonics. With a peak processing temperature less than 300$^\circ$C, it is back-end compatible with pre-processed CMOS substrates. We achieve microresonators with a quality factor of up to $1.6\times 10^6 $ at 1552 nm, and $>1.2\times 10^6$ throughout $\lambda$ = 1510 -- 1600 nm, without annealing or stress management. We then demonstrate the immediate utility of this platform in nonlinear photonics by generating a 1 THz free spectral range, 900-nm-bandwidth modulation-instability microresonator Kerr comb and octave-spanning, supercontinuum-broadened spectra.

We propose and experimentally demonstrate a photonic routing architecture that can efficiently utilize the space of multi-plane (3D) photonic integration. A wafer with three planes of amorphous silicon waveguides was fabricated and characterized, demonstrating $<3\times10^{-4}$ dB loss per out-of-plane waveguide crossing, $0.05 \pm 0.02 $ dB per interplane coupler, and microring resonators on three planes with a quality factors up to $8.2 \times 10^{4}$. We also explore a phase velocity mapping strategy to mitigate the crosstalk between co-propagating waveguides on different planes. These results expand the utility of 3D photonic integration for applications such as optical interconnects, neuromorphic computing and optical phased arrays.

Infrared spectroscopy is a powerful tool for basic and applied science. The molecular spectral fingerprints in the 3 um to 20 um region provide a means to uniquely identify molecular structure for fundamental spectroscopy, atmospheric chemistry, trace and hazardous gas detection, and biological microscopy. Driven by such applications, the development of low-noise, coherent laser sources with broad, tunable coverage is a topic of great interest. Laser frequency combs possess a unique combination of precisely defined spectral lines and broad bandwidth that can enable the above-mentioned applications. Here, we leverage robust fabrication and geometrical dispersion engineering of silicon nanophotonic waveguides for coherent frequency comb generation spanning 70 THz in the mid-infrared (2.5 um to 6.2 um). Precise waveguide fabrication provides significant spectral broadening and engineered spectra targeted at specific mid-infrared bands. We use this coherent light source for dual-comb spectroscopy at 5 um., Comment: 26 pages, 5 figures

We demonstrate cryogenic, electrically-injected, waveguide-coupled Si light-emitting diodes (LEDs) operating at 1.22 $\mu$m. The active region of the LED consists of W centers implanted in the intrinsic region of a $p$-$i$-$n$ diode. The LEDs are integrated on waveguides with superconducting nanowire single-photon detectors (SNSPDs). We demonstrate the scalability of this platform with an LED coupled to eleven SNSPDs in a single integrated photonic device. Such on-chip optical links may be useful for quantum information or neuromorphic computing applications.

We present an approach to fabrication and packaging of integrated photonic devices that utilizes waveguide and detector layers deposited at near-ambient temperature. All lithography is performed with a 365 nm i-line stepper, facilitating low cost and high scalability. We have shown low-loss SiN waveguides, high-$Q$ ring resonators, critically coupled ring resonators, 50/50 beam splitters, Mach-Zehnder interferometers (MZIs) and a process-agnostic fiber packaging scheme. We have further explored the utility of this process for applications in nonlinear optics and quantum photonics. We demonstrate spectral tailoring and octave-spanning supercontinuum generation as well as the integration of superconducting nanowire single photon detectors with MZIs and channel-dropping filters. The packaging approach is suitable for operation up to 160 \degree C as well as below 1 K. The process is well suited for augmentation of existing foundry capabilities or as a stand-alone process., Comment: 13 pages, 7 figures

Neural networks have proven effective for solving many difficult computational problems. Implementing complex neural networks in software is very computationally expensive. To explore the limits of information processing, it will be necessary to implement new hardware platforms with large numbers of neurons, each with a large number of connections to other neurons. Here we propose a hybrid semiconductor-superconductor hardware platform for the implementation of neural networks and large-scale neuromorphic computing. The platform combines semiconducting few-photon light-emitting diodes with superconducting-nanowire single-photon detectors to behave as spiking neurons. These processing units are connected via a network of optical waveguides, and variable weights of connection can be implemented using several approaches. The use of light as a signaling mechanism overcomes fanout and parasitic constraints on electrical signals while simultaneously introducing physical degrees of freedom which can be employed for computation. The use of supercurrents achieves the low power density necessary to scale to systems with enormous entropy. The proposed processing units can operate at speeds of at least $20$ MHz with fully asynchronous activity, light-speed-limited latency, and power densities on the order of 1 mW/cm$^2$ for neurons with 700 connections operating at full speed at 2 K. The processing units achieve an energy efficiency of $\approx 20$ aJ per synapse event. By leveraging multilayer photonics with deposited waveguides and superconductors with feature sizes $>$ 100 nm, this approach could scale to systems with massive interconnectivity and complexity for advanced computing as well as explorations of information processing capacity in systems with an enormous number of information-bearing microstates., Comment: 34 pages, 22 figures

Correlated photon pairs are a fundamental building block of quantum photonic systems. While pair sources have previously been integrated on silicon chips built using customized photonics manufacturing processes, these often take advantage of only a small fraction of the established techniques for microelectronics fabrication and have yet to be integrated in a process which also supports electronics. Here we report the first demonstration of quantum-correlated photon pair generation in a device fabricated in an unmodified advanced (sub-100nm) complementary metal-oxide-semiconductor (CMOS) process, alongside millions of working transistors. The microring resonator photon pair source is formed in the transistor layer structure, with the resonator core formed by the silicon layer typically used for the transistor body. With ultra-low continuous-wave on-chip pump powers ranging from 5 $\mu$W to 400 $\mu$W, we demonstrate pair generation rates between 165 Hz and 332 kHz using >80% efficient WSi superconducting nanowire single photon detectors. Coincidences-to-accidentals ratios consistently exceeding 40 were measured with a maximum of 55. In the process of characterizing this source we also accurately predict pair generation rates from the results of classical four-wave mixing measurements. This proof-of-principle device demonstrates the potential of commercial CMOS microelectronics as an advanced quantum photonics platform with capability of large volume, pristine process control, and where state-of-the-art high-speed digital circuits could interact with quantum photonic circuits.

We demonstrate the first monolithically integrated linear photonic crystal microcavities in an advanced SOI CMOS microelectronics process (IBM 45nm 12SOI) with no in-foundry process modifications. The cavities were integrated into a standard microelectronics design flow meeting process design rules, and fabricated alongside transistors native to the process. We demonstrate both 1520nm wavelength and 1180nm cavity designs using different cavity implementations due to design rule constraints. For the 1520nm and 1180nm designs, loaded quality factors of 2,000 and 4,000 are measured, and intrinsic quality factors of 100,000 and 60,000 are extracted. We also demonstrate an evanescent coupling geometry which decouples the cavity and waveguide-coupling design., Comment: 4 pages, 6 figures

We experimentally demonstrate broadband waveguide crossing arrays showing ultra low loss down to $0.04\,$dB/crossing ($0.9\%$), matching theory, and crosstalk suppression over $35\,$dB, in a CMOS-compatible geometry. The principle of operation is the tailored excitation of a low-loss spatial Bloch wave formed by matching the periodicity of the crossing array to the difference in propagation constants of the 1$^\text{st}$- and 3$^\text{rd}$-order TE-like modes of a multimode silicon waveguide. Radiative scattering at the crossing points acts like a periodic imaginary-permittivity perturbation that couples two supermodes, which results in imaginary (radiative) propagation-constant splitting and gives rise to a low-loss, unidirectional breathing Bloch wave. This type of crossing array provides a robust implementation of a key component enabling dense photonic integration.

Within the theory of Siegert pseudostates, it is possible to accurately calculate bound states and resonances. The energy continuum is replaced by a discrete set of states. Many questions of interest in scattering theory can be addressed within the framework of this formalism, thereby avoiding the need to treat the energy continuum. For practical calculations it is important to know whether a certain subset of Siegert pseudostates comprises a basis. This is a nontrivial issue, because of the unusual orthogonality and overcompleteness properties of Siegert pseudostates. Using analytical and numerical arguments, it is shown that the subset of bound states and outgoing Siegert pseudostates forms a basis. Time evolution in the context of Siegert pseudostates is also investigated. From the Mittag-Leffler expansion of the outgoing-wave Green's function, the time-dependent expansion of a wave packet in terms of Siegert pseudostates is derived. In this expression, all Siegert pseudostates--bound, antibound, outgoing, and incoming--are employed. Each of these evolves in time in a nonexponential fashion. Numerical tests underline the accuracy of the method.