Study of all-group-IV SiGeSn mid-IR lasers with dual wavelength emission.

Direct band gap GeSn alloys have recently emerged as promising lasing source materials for monolithic integration on Si substrate. In this work, optically pumped mid-infrared GeSn lasers were studied with the observation of dual-wavelength lasing at 2187 nm and 2460 nm. Two simultaneous lasing regions include a GeSn buffer layer (bulk) and a SiGeSn/GeSn multiple quantum well structure that were grown seamlessly using a chemical vapor deposition reactor. The onset of dual lasing occurs at 420 kW/cm2. The wider bandgap SiGeSn partitioning barrier enables the independent operation of two gain regions. While the better performance device in terms of lower threshold may be obtained by using two MQW regions design, the preliminary results and discussions in this work paves a way towards all-group-IV dual wavelength lasers monolithically integrated on Si substrate.

Divisive normalization processors in the early visual system of the Drosophila brain.

Divisive normalization is a model of canonical computation of brain circuits. We demonstrate that two cascaded divisive normalization processors (DNPs), carrying out intensity/contrast gain control and elementary motion detection, respectively, can model the robust motion detection realized by the early visual system of the fruit fly. We first introduce a model of elementary motion detection and rewrite its underlying phase-based motion detection algorithm as a feedforward divisive normalization processor. We then cascade the DNP modeling the photoreceptor/amacrine cell layer with the motion detection DNP. We extensively evaluate the DNP for motion detection in dynamic environments where light intensity varies by orders of magnitude. The results are compared to other bio-inspired motion detectors as well as state-of-the-art optic flow algorithms under natural conditions. Our results demonstrate the potential of DNPs as canonical building blocks modeling the analog processing of early visual systems. The model highlights analog processing for accurately detecting visual motion, in both vertebrates and invertebrates. The results presented here shed new light on employing DNP-based algorithms in computer vision.

Investigation of the cap layer for improved GeSn multiple quantum well laser performance.

The study of all-group-IV SiGeSn lasers has opened a new avenue to Si-based light sources. SiGeSn heterostructure and quantum well lasers have been successfully demonstrated in the past few years. It has been reported that, for multiple quantum well lasers, the optical confinement factor plays an important role in the net modal gain. In previous studies, adding a cap layer was proposed to increase the optical mode overlap with the active region and thereby improve the optical confinement factor of Fabry-Perot cavity lasers. In this work, SiGeSn/GeSn multiple quantum well (4-well) devices with various cap layer thicknesses, i.e., 0 (no cap), 190, 250, and 290 nm, are grown using a chemical vapor deposition reactor and characterized via optical pumping. While no-cap and thinner-cap devices only show spontaneous emission, the two thicker-cap devices exhibit lasing up to 77 K, with an emission peak at 2440 nm and a threshold of 214 kW/cm2 (250 nm cap device). The clear trend in device performance disclosed in this work provides guidance in device design for electrically injected SiGeSn quantum well lasers.

A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain.

The Drosophila brain has only a fraction of the number of neurons of higher organisms such as mice and humans. Yet the sheer complexity of its neural circuits recently revealed by large connectomics datasets suggests that computationally modeling the function of fruit fly brain circuits at this scale poses significant challenges. To address these challenges, we present here a programmable ontology that expands the scope of the current Drosophila brain anatomy ontologies to encompass the functional logic of the fly brain. The programmable ontology provides a language not only for modeling circuit motifs but also for programmatically exploring their functional logic. To achieve this goal, we tightly integrated the programmable ontology with the workflow of the interactive FlyBrainLab computing platform. As part of the programmable ontology, we developed NeuroNLP++, a web application that supports free-form English queries for constructing functional brain circuits fully anchored on the available connectome/synaptome datasets, and the published worldwide literature. In addition, we present a methodology for including a model of the space of odorants into the programmable ontology, and for modeling olfactory sensory circuits of the antenna of the fruit fly brain that detect odorant sources. Furthermore, we describe a methodology for modeling the functional logic of the antennal lobe circuit consisting of a massive number of local feedback loops, a characteristic feature observed across Drosophila brain regions. Finally, using a circuit library, we demonstrate the power of our methodology for interactively exploring the functional logic of the massive number of feedback loops in the antennal lobe.

Investigation of SiGeSn/GeSn/SiGeSn single quantum well with enhanced well emission.

In this work, a SiGeSn/GeSn/SiGeSn single quantum well was grown and characterized. The sample has a thicker GeSn well of 22nm compared to a previously reported 9nm well configuration. The thicker well leads to: (i) lowered ground energy level in Γ valley offering more bandgap directness; (ii) increased carrier density in the well; and (iii) improved carrier collection due to increased barrier height. As a result, significantly enhanced emission from the quantum well was observed. The strong photoluminescence (PL) signal allows for the estimation of quantum efficiency (QE), which was unattainable in previous studies. Using pumping-power-dependent PL spectra at 20K, the peak spontaneous QE and external QE were measured as 37.9% and 1.45%, respectively.

Accelerating with FlyBrainLab the discovery of the functional logic of the Drosophila brain in the connectomic and synaptomic era.

In recent years, a wealth of Drosophila neuroscience data have become available including cell type and connectome/synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fruit fly brain, we have developed FlyBrainLab, a unique open-source computing platform that integrates 3D exploration and visualization of diverse datasets with interactive exploration of the functional logic of modeled executable brain circuits. FlyBrainLab's User Interface, Utilities Libraries and Circuit Libraries bring together neuroanatomical, neurogenetic and electrophysiological datasets with computational models of different researchers for validation and comparison within the same platform. Seeking to transcend the limitations of the connectome/synaptome, FlyBrainLab also provides libraries for molecular transduction arising in sensory coding in vision/olfaction. Together with sensory neuron activity data, these libraries serve as entry points for the exploration, analysis, comparison, and evaluation of circuit functions of the fruit fly brain.

Sparse identification of contrast gain control in the fruit fly photoreceptor and amacrine cell layer.

The fruit fly's natural visual environment is often characterized by light intensities ranging across several orders of magnitude and by rapidly varying contrast across space and time. Fruit fly photoreceptors robustly transduce and, in conjunction with amacrine cells, process visual scenes and provide the resulting signal to downstream targets. Here, we model the first step of visual processing in the photoreceptor-amacrine cell layer. We propose a novel divisive normalization processor (DNP) for modeling the computation taking place in the photoreceptor-amacrine cell layer. The DNP explicitly models the photoreceptor feedforward and temporal feedback processing paths and the spatio-temporal feedback path of the amacrine cells. We then formally characterize the contrast gain control of the DNP and provide sparse identification algorithms that can efficiently identify each the feedforward and feedback DNP components. The algorithms presented here are the first demonstration of tractable and robust identification of the components of a divisive normalization processor. The sparse identification algorithms can be readily employed in experimental settings, and their effectiveness is demonstrated with several examples.

Ge1-xSnx alloys: Consequences of band mixing effects for the evolution of the band gap Γ-character with Sn concentration.

In this work we study the nature of the band gap in GeSn alloys for use in silicon-based lasers. Special attention is paid to Sn-induced band mixing effects. We demonstrate from both experiment and ab-initio theory that the (direct) Γ-character of the GeSn band gap changes continuously with alloy composition and has significant Γ-character even at low (6%) Sn concentrations. The evolution of the Γ-character is due to Sn-induced conduction band mixing effects, in contrast to the sharp indirect-to-direct band gap transition obtained in conventional alloys such as Al1-xGaxAs. Understanding the band mixing effects is critical not only from a fundamental and basic properties viewpoint but also for designing photonic devices with enhanced capabilities utilizing GeSn and related material systems.

Optically pumped lasing at 3 µm from compositionally graded GeSn with tin up to 22.3.

The recent demonstration of the GeSn laser opened a promising route towards the monolithic integration of light sources on the Si platform. A GeSn laser with higher Sn content is highly desirable to enhance the emission efficiency and to cover longer wavelength. This Letter reports optically pumped edge-emitting GeSn lasers operating at 3 µm, whose device structure featured Sn compositionally graded with a maximum Sn content of 22.3%. By using a 1950-nm laser pumping in comparison with a 1064-nm pumping, the local heating and quantum defect were effectively reduced, which improved laser performance in terms of higher maximum lasing temperature and lower threshold.

Study of direct bandgap type-I GeSn/GeSn double quantum well with improved carrier confinement.

The GeSn-based quantum wells (QWs) have been investigated recently for the development of efficient GeSn emitters. Although our previous study indicated that the direct bandgap well with type-I band alignment was achieved, the demonstrated QW still has insufficient carrier confinement. In this work, we report the systematic study of light emission from the Ge0.91Sn0.09/Ge0.85Sn0.15/Ge0.91Sn0.09 double QW structure. Two double QW samples, with the thicknesses of Ge0.85Sn0.15 well of 6 and 19 nm, were investigated. Band structure calculations revealed that both samples feature type-I band alignment. Compared with our previous study, by increasing the Sn composition in GeSn barrier and well, the QW layer featured increased energy separation between the indirect and direct bandgaps towards a better direct gap semiconductor. Moreover, the thicker well sample exhibited improved carrier confinement compared to the thinner well sample due to lowered first quantized energy level in the Γ valley. To identify the optical transition characteristics, photoluminescence (PL) study using three pump lasers with different penetration depths and photon energies was performed. The PL spectra confirmed the direct bandgap well feature and the improved carrier confinement, as significantly enhanced QW emission from the thicker well sample was observed.

Investigation of GeSn Strain Relaxation and Spontaneous Composition Gradient for Low-Defect and High-Sn Alloy Growth.

Recent development of group-IV alloy GeSn indicates its bright future for the application of mid-infrared Si photonics. Relaxed GeSn with high material quality and high Sn composition is highly desirable to cover mid-infrared wavelength. However, its crystal growth remains a great challenge. In this work, a systematic study of GeSn strain relaxation mechanism and its effects on Sn incorporation during the material growth via chemical vapor deposition was conducted. It was discovered that Sn incorporation into Ge lattice sites is limited by high compressive strain rather than historically acknowledged chemical reaction dynamics, which was also confirmed by Gibbs free energy calculation. In-depth material characterizations revealed that: (i) the generation of dislocations at Ge/GeSn interface eases the compressive strain, which offers a favorably increased Sn incorporation; (ii) the formation of dislocation loop near Ge/GeSn interface effectively localizes defects, leading to the subsequent low-defect grown GeSn. Following the discovered growth mechanism, a world-record Sn content of 22.3% was achieved. The experiment result shows that even higher Sn content could be obtained if further continuous growth with the same recipe is conducted. This report offers an essential guidance for the growth of high quality high Sn composition GeSn for future GeSn based optoelectronics.

Sparse Functional Identification of Complex Cells from Spike Times and the Decoding of Visual Stimuli.

We investigate the sparse functional identification of complex cells and the decoding of spatio-temporal visual stimuli encoded by an ensemble of complex cells. The reconstruction algorithm is formulated as a rank minimization problem that significantly reduces the number of sampling measurements (spikes) required for decoding. We also establish the duality between sparse decoding and functional identification and provide algorithms for identification of low-rank dendritic stimulus processors. The duality enables us to efficiently evaluate our functional identification algorithms by reconstructing novel stimuli in the input space. Finally, we demonstrate that our identification algorithms substantially outperform the generalized quadratic model, the nonlinear input model, and the widely used spike-triggered covariance algorithm.

Study of a SiGeSn/GeSn/SiGeSn structure toward direct bandgap type-I quantum well for all group-IV optoelectronics.

A SiGeSn/GeSn/SiGeSn single quantum well structure was grown using an industry standard chemical vapor deposition reactor with low-cost commercially available precursors. The material characterization revealed the precisely controlled material growth process. Temperature-dependent photoluminescence spectra were correlated with band structure calculation for a structure accurately determined by high-resolution x-ray diffraction and transmission electron microscopy. Based on the result, a systematic study of SiGeSn and GeSn bandgap energy separation and barrier heights versus material compositions and strain was conducted, leading to a practical design of a type-I direct bandgap quantum well.

A Motion Detection Algorithm Using Local Phase Information.

Previous research demonstrated that global phase alone can be used to faithfully represent visual scenes. Here we provide a reconstruction algorithm by using only local phase information. We also demonstrate that local phase alone can be effectively used to detect local motion. The local phase-based motion detector is akin to models employed to detect motion in biological vision, for example, the Reichardt detector. The local phase-based motion detection algorithm introduced here consists of two building blocks. The first building block measures/evaluates the temporal change of the local phase. The temporal derivative of the local phase is shown to exhibit the structure of a second order Volterra kernel with two normalized inputs. We provide an efficient, FFT-based algorithm for implementing the change of the local phase. The second processing building block implements the detector; it compares the maximum of the Radon transform of the local phase derivative with a chosen threshold. We demonstrate examples of applying the local phase-based motion detection algorithm on several video sequences. We also show how the locally detected motion can be used for segmenting moving objects in video scenes and compare our local phase-based algorithm to segmentation achieved with a widely used optic flow algorithm.

Massively parallel neural circuits for stereoscopic color vision: encoding, decoding and identification.

Past work demonstrated how monochromatic visual stimuli could be faithfully encoded and decoded under Nyquist-type rate conditions. Color visual stimuli were then traditionally encoded and decoded in multiple separate monochromatic channels. The brain, however, appears to mix information about color channels at the earliest stages of the visual system, including the retina itself. If information about color is mixed and encoded by a common pool of neurons, how can colors be demixed and perceived? We present Color Video Time Encoding Machines (Color Video TEMs) for encoding color visual stimuli that take into account a variety of color representations within a single neural circuit. We then derive a Color Video Time Decoding Machine (Color Video TDM) algorithm for color demixing and reconstruction of color visual scenes from spikes produced by a population of visual neurons. In addition, we formulate Color Video Channel Identification Machines (Color Video CIMs) for functionally identifying color visual processing performed by a spiking neural circuit. Furthermore, we derive a duality between TDMs and CIMs that unifies the two and leads to a general theory of neural information representation for stereoscopic color vision. We provide examples demonstrating that a massively parallel color visual neural circuit can be first identified with arbitrary precision and its spike trains can be subsequently used to reconstruct the encoded stimuli. We argue that evaluation of the functional identification methodology can be effectively and intuitively performed in the stimulus space. In this space, a signal reconstructed from spike trains generated by the identified neural circuit can be compared to the original stimulus.

Volterra dendritic stimulus processors and biophysical spike generators with intrinsic noise sources.

We consider a class of neural circuit models with internal noise sources arising in sensory systems. The basic neuron model in these circuits consists of a dendritic stimulus processor (DSP) cascaded with a biophysical spike generator (BSG). The dendritic stimulus processor is modeled as a set of nonlinear operators that are assumed to have a Volterra series representation. Biophysical point neuron models, such as the Hodgkin-Huxley neuron, are used to model the spike generator. We address the question of how intrinsic noise sources affect the precision in encoding and decoding of sensory stimuli and the functional identification of its sensory circuits. We investigate two intrinsic noise sources arising (i) in the active dendritic trees underlying the DSPs, and (ii) in the ion channels of the BSGs. Noise in dendritic stimulus processing arises from a combined effect of variability in synaptic transmission and dendritic interactions. Channel noise arises in the BSGs due to the fluctuation of the number of the active ion channels. Using a stochastic differential equations formalism we show that encoding with a neuron model consisting of a nonlinear DSP cascaded with a BSG with intrinsic noise sources can be treated as generalized sampling with noisy measurements. For single-input multi-output neural circuit models with feedforward, feedback and cross-feedback DSPs cascaded with BSGs we theoretically analyze the effect of noise sources on stimulus decoding. Building on a key duality property, the effect of noise parameters on the precision of the functional identification of the complete neural circuit with DSP/BSG neuron models is given. We demonstrate through extensive simulations the effects of noise on encoding stimuli with circuits that include neuron models that are akin to those commonly seen in sensory systems, e.g., complex cells in V1.

The power of connectivity: identity preserving transformations on visual streams in the spike domain.

We investigate neural architectures for identity preserving transformations (IPTs) on visual stimuli in the spike domain. The stimuli are encoded with a population of spiking neurons; the resulting spikes are processed and finally decoded. A number of IPTs are demonstrated including faithful stimulus recovery, as well as simple transformations on the original visual stimulus such as translations, rotations and zoomings. We show that if the set of receptive fields satisfies certain symmetry properties, then IPTs can easily be realized and additionally, the same basic stimulus decoding algorithm can be employed to recover the transformed input stimulus. Using group theoretic methods we advance two different neural encoding architectures and discuss the realization of exact and approximate IPTs. These are realized in the spike domain processing block by a "switching matrix" that regulates the input/output connectivity between the stimulus encoding and decoding blocks. For example, for a particular connectivity setting of the switching matrix, the original stimulus is faithfully recovered. For other settings, translations, rotations and dilations (or combinations of these operations) of the original video stream are obtained. We evaluate our theoretical derivations through extensive simulations on natural video scenes, and discuss implications of our results on the problem of invariant object recognition in the spike domain.

Massively parallel neural encoding and decoding of visual stimuli.

The massively parallel nature of video Time Encoding Machines (TEMs) calls for scalable, massively parallel decoders that are implemented with neural components. The current generation of decoding algorithms is based on computing the pseudo-inverse of a matrix and does not satisfy these requirements. Here we consider video TEMs with an architecture built using Gabor receptive fields and a population of Integrate-and-Fire neurons. We show how to build a scalable architecture for video Time Decoding Machines using recurrent neural networks. Furthermore, we extend our architecture to handle the reconstruction of visual stimuli encoded with massively parallel video TEMs having neurons with random thresholds. Finally, we discuss in detail our algorithms and demonstrate their scalability and performance on a large scale GPU cluster.

Encoding natural scenes with neural circuits with random thresholds.

We present a general framework for the reconstruction of natural video scenes encoded with a population of spiking neural circuits with random thresholds. The natural scenes are modeled as space-time functions that belong to a space of trigonometric polynomials. The visual encoding system consists of a bank of filters, modeling the visual receptive fields, in cascade with a population of neural circuits, modeling encoding in the early visual system. The neuron models considered include integrate-and-fire neurons and ON-OFF neuron pairs with threshold-and-fire spiking mechanisms. All thresholds are assumed to be random. We demonstrate that neural spiking is akin to taking noisy measurements on the stimulus both for time-varying and space-time-varying stimuli. We formulate the reconstruction problem as the minimization of a suitable cost functional in a finite-dimensional vector space and provide an explicit algorithm for stimulus recovery. We also present a general solution using the theory of smoothing splines in Reproducing Kernel Hilbert Spaces. We provide examples of both synthetic video as well as for natural scenes and demonstrate that the quality of the reconstruction degrades gracefully as the threshold variability of the neurons increases.