GeSn Light-Emitting Devices

Silicon based optoelectronic devices have been investigated for decades. However, due to the indirect band gap nature of Si and Ge, developing of efficient light-emitting source on Si is still a challenging topic. GeSn based optoelectronic devices have the great potential to overcome this deficiency for several reasons. By adding more fraction of Sn into Ge, GeSn band gap could be reduced. The narrowed band gap could be developed for near to mid infrared applications. The alloy can even become the direct band gap material with a large Sn composition (beyond 8%). This feature could enhance the light emission from the direct band gap transition. Due to the simple process of GeSn device fabrication, the cost of infrared optoelectronic devices could be reduced. Furthermore, the compatibility of GeSn based devices on complementary metal on semiconductor (CMOS) process enables further opportunities for Si photonic integrated circuits. This thesis discusses the fabrication and characterization of GeSn optoelectronic devices to prove the great potential of this material. The discussion mainly covers the double hetrostructure (DHS) LED, following with an extension study on photodetector. The grown material was characterized and proved to be high quality using X-Ray diffraction (XRD) and photoluminescence (PL). The LED fabrication process and results are described in detail. Surface emitting LED characterization was studied using the current-voltage (I-V) measurement, electroluminescence (EL), as well as optical power. EL spectra of 6%, 8%, 9%, and 10% Sn LED was measured. Emission due to the direct band was observed. The wavelength of the EL spectrum peak of 2348 nm was achieved for measuring 10% Sn LED. Optical power with an average of 0.2 mW was measured under 100 mA current injection. Surface emitting LED design was developed into three generations serving for different research purposes. Edge emitting LED was fabricated and characterized with I-V and EL measurements. For light-detection, both photoconductors and p-i-n photodiodes were characterized with I-V and the spectral response. The absorption spectral response was measured with different Sn composition devices, showing the extended detection range towards mid infrared. The characterizations of GeSn based optoelectronic devices in this thesis demonstrated the GeSn material is versatile and capable for optoelectronic devices

SiGeSn Light-Emitting Devices: from Optical to Electrical Injection

Si photonics is a fast-developing technology that impacts many applications such as data centers, 5G, Lidar, and biological/chemical sensing. One of the merits of Si photonics is to integrate electronic and photonic components on a single chip to form a complex functional system that features compact, low-cost, high-performance, and reliability. Among all building blocks, the monolithic integration of lasers on Si encountered substantial challenges. Si and Ge, conventional epitaxial material on Si, are incompetent for light emission due to the indirect bandgap. The current solution compromises the hybrid integration of III-V lasers, which requires growing on separate smaller size substrates and bonded on Si wafers. The monolithic growth of III-V lasers suffers from high-density defects and the growth temperature incompatible with the complementary-metal-on-semiconductor (CMOS) process. Therefore, alternative solutions are of high interest to overcome such difficulties. SiGeSn is a Group-IV semiconductor that could achieve direct bandgap, monolithically grown on Si substrate, and CMOS process compatible. These advantages make SiGeSn rather promising towards the monolithic laser for Si photonics. This dissertation presents the multiple efforts on developing the GeSn-based lasers towards the electrical injection. The development process starts with the bulk lasers by optically pumping. By incorporating Sn in the active region and leaving the threading dislocation away from the active region, the maximum operating temperature (Tmax) of the broad ridge laser reached 270 K with 20% Sn in the GeSn active region. The lasers with the multiple-quantum-well (MQW) as the gain region were studied for reducing the threshold. The results implied a sufficient gain volume was required to overcome the existing loss within the device. The laser structure with four wells exhibited lasing at temperatures up to 90 K. The introduction of the SiGeSn cap layer balance more optical field overlapping the MQW active region, leading to an increase of Tmax. By adding the quantum well number, the lasers showed improvement in the modal gain, eventually reducing the threshold and elevating the Tmax. The study of light-emitting diodes provides the insight of GeSn heterostructures before achieving the electrically injected GeSn lasers. The three developing structures including Ge/GeSn/Ge, GeSn homojunction, and GeSn/GeSn/SiGeSn heterostructures were designed for: (1) achieving direct bandgap in GeSn active region, (2) incorporating high Sn composition and maintaining strain relaxation in the active region, and (3) eliminating carrier leakage through the hole barriers. With the advances in the GeSn heterostructures, the layer structures were applied to the electrically injected lasers. The electrically injected lasing from GeSn was demonstrated at temperatures up to 100 K. The laser diode structures were further investigated by comparing the layer material and thickness, providing further suggestions on optimizing the laser design

Massively Parallel Spiking Neural Circuits: Encoding, Decoding and Functional Identification

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

SiGeSn laser diodes and method of fabricating same

Description and specifications of a new and distinct cultivar of blackberry plant named ‘APF-404T’ which originated from seed produced by a hand-pollinated cross of Arkansas selections ‘APF-185T’ (a non-patented, unreleased breeding selection) x ‘A-2444T’ (a non-patented, unreleased breeding selection) is provided. This new cultivar of blackberry plant can be distinguished by its thornless canes, primocane-fruiting tendency, and large, sweet fruit. The plants have consistently good plant health and produce fruit suitable for home gardens

SiGeSn laser diodes and method of fabricating same

Description and specifications of a new and distinct cultivar of blackberry plant named ‘APF-404T’ which originated from seed produced by a hand-pollinated cross of Arkansas selections ‘APF-185T’ (a non-patented, unreleased breeding selection) x ‘A-2444T’ (a non-patented, unreleased breeding selection) is provided. This new cultivar of blackberry plant can be distinguished by its thornless canes, primocane-fruiting tendency, and large, sweet fruit. The plants have consistently good plant health and produce fruit suitable for home gardens

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

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

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