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The significant progress of GeSn material development has enabled a feasible solution to the long-desired monolithically integrated lasers on the Si platform. While there are many reports focused on optically pumped lasers, GeSn lasers through electrical injection have not been experimentally achieved yet. In this work, we report the first demonstration of electrically injected GeSn lasers on Si. A GeSn/SiGeSn heterostructure diode grown on a Si substrate was fabricated into ridge waveguide laser devices and tested under pulsed conditions. Special considerations were given for the structure design to ensure effective carrier confinement and optical confinement that lead to lasing. Lasing was observed at temperatures from 10 to 100 K with emission peaks at around 2300 nm. The minimum threshold of 598 A/cm2 was recorded at 10 K and the threshold increased to 842 A/cm2 at 77 K. The spectral linewidth of a single peak was measured as small as 0.13 nm (0.06 meV). The maximum characteristic temperature was extracted as 99 K over the temperature range of 10-77 K.

This paper reports a comprehensive study of Si-based GeSn mid-infrared photodetectors, which includes: 1) the demonstration of a set of photoconductors with Sn compositions ranging from 10.5% to 22.3%, showing the cut-off wavelength has been extended to 3.65 um. The measured maximum D* of 1.1x10^10 cmHz^(1/2)W(-1) is comparable to that of commercial extended-InGaAs detectors; 2) the development of surface passivation technique on photodiode based on in-depth analysis of dark current mechanism, effectively reducing the dark current. Moreover, mid-infrared images were obtained using GeSn photodetectors, showing the comparable image quality with that acquired by using commercial PbSe detectors., Comment: 25 pages, 8 figures

A Si-based monolithic laser is highly desirable for full integration of Si-photonics. Lasing from direct bandgap group-IV GeSn alloy has opened a completely new venue from the traditional III-V integration approach. We demonstrated optically pumped GeSn lasers on Si with broad wavelength coverage from 2 to 3 ��m. The GeSn alloys were grown using newly developed approaches with an industry standard chemical vapor deposition reactor and low-cost commercially available precursors. The achieved maximum Sn composition of 17.5% exceeded the generally acknowledged Sn incorporation limits for using similar deposition chemistries. The highest lasing temperature was measured as 180 K with the active layer thickness as thin as 260 nm. The unprecedented lasing performance is mainly due to the unique growth approaches, which offer high-quality epitaxial materials. The results reported in this work show a major advance towards Si-based mid-infrared laser sources for integrated photonics., 34 pages, 12 figures

The DrosophilaRetina has been extensively characterized in the literature in terms of structure,connectivity and function.We present here a detailed description of the algorithms required for afull-scaleparallel emulation of the Retinaincluding (i) the mappingof the visual field onto the photoreceptors, (ii) thephototransductionprocess, and (iii) the parallel processing of the visual field by theentire Retina.We also provide detailed algorithms, theirimplementation and their visual evaluation.

This thesis presents a class of massively parallel spiking neural circuit architectures in which neurons are modeled by dendritic stimulus processors cascaded with spike generators. We investigate how visual stimuli can be represented by the spike times generated by the massively parallel neural circuits, how the spike times can be used to reconstruct and process visual stimuli, and the conditions when visual stimuli can be faithfully represented/reconstructed. Functional identification of the massively parallel neural circuits from spike times and its evaluation are also investigated. Together, this thesis offers a comprehensive analytic framework of massively parallel spiking neural circuit architectures arising in the study of early visual systems. In encoding, modeling of visual stimuli in reproducing kernel Hilbert spaces is presented, recognizing the importance of studying visual encoding in a rigorous mathematical framework. For massively parallel neural circuits with biophysical spike generators, I/O characterization of the biophysical spike generators becomes possible by introducing phase response curve manifolds for the biophysical spike generators. I/O characterization of the entire neural circuit can then be interpreted as generalized sampling in the Hilbert space. Multi-component dendritic stimulus processors are introduced to model visual encoding in stereoscopic color vision. It is also shown that encoding of visual stimuli by an ensemble of complex cells has the complexity of Volterra dendritic stimulus processors. Based on the I/O characterization, reconstruction algorithms are derived to decode, from spike times, visual stimuli encoded by these massively parallel neural circuits. Decoding problems are first formulated as spline interpolation problems. Conditions on faithful reconstruction are presented, allowing the probe of information content carried by the spikes. Algorithms are developed to qualify the decoding in massively parallel settings. For stereoscopic color visual stimuli, demixing of individual channels from an unlabeled set of spike trains is demonstrated. For encoding with complex cells, decoding problems are formulated as rank minimization problems. It is shown that the decoding algorithm does not suffer from the curse of dimensionality and thereby allows for a visual representation using biologically realistic neural resources. The study of visual stimuli encoding and decoding enables the functional identification of massively parallel neural circuits. The duality between decoding and functional identification suggests that algorithms for functional identification of the projection of dendritic stimulus processors onto the space of input stimuli can be formulated similarly to the decoding algorithms. Functional identification of dendritic stimulus processors of neurons carrying stereoscopic color information as well as that of energy processing in complex cells is demonstrated. Furthermore, this duality also inspires a novel method to evaluate the quality of functional identification of massively parallel spiking neural circuits. By reconstructing novel stimuli using identified circuit parameters, the evaluation of the entire identified circuit is reduced to intuitive comparisons in stimulus space. The use of biophysical spike generators advances a methodology in the study of intrinsic noise sources in neurons and their effects on stimulus representation and on precision of functional identification. These effects are investigated using a class of nonlinear neural circuits consisting of both feedforward and feedback Volterra dendritic stimulus processors and biophysical spike generators. It is shown that encoding with neural circuits with intrinsic noise sources can be interpreted as generalized sampling with noisy measurements. Effects of noise on decoding and functional identification are derived theoretically and were systematically investigated by extensive simulations. Finally, the massively parallel neural circuit architectures are shown to enable the implementation of identity preserving transformations in the spike domain using a switching matrix regulating the connection between encoding and decoding. Two realizations of the architectures are developed, and extensive examples using continuous visual streams are provided. Implications of this result on the problem of invariant object recognition in the spike domain are discussed.

The Lamina is a neuropil that resides in the optic lobeof the early visual system of the fruit fly.Its neural circuit consists of some700~800cartridges.A cartridge is an atomic neural circuitabstraction whose I/O behavior can be studied in isolation.The anatomy of the neurons,neurotransmitter types andconnectivity patterns of the neurons in thecartridge are briefly reviewed.A circuit-level model of a cartridge suitable forimplementationon a GPU platform is presented in detail.Cartridge interconnects create neural circuits with differentI/O characteristics.A number of cartridge composition rules for buildingneural circuits with different spatial I/O characteristics are discussed.We present a model neural circuit for initial functional evaluations and scaling of the Lamina and its interconnection with the Medulla.Implementation considerations of the Lamina neuralcircuit are discussed.