Versatile spectral imaging with an algorithm-based spectrometer using highly tuneable quantum dot infrared photodetectors

We report on the implementation of an algorithm-based spectrometer capable of reconstructing the spectral shape of materials in the mid-wave infrared (MWIR) and long-wave infrared (LWIR) wavelengths using only experimental photocurrent measurements from quantum dot infrared photodetectors (QDIPs). The theory and implementation of the algorithm will be described, followed by an investigation into this algorithmic spectrometer's performance. Compared to the QDIPs utilized in an earlier implementation, the ones used here have highly varying spectral shapes and four spectral peaks across the MWIR and LWIR wavelengths. It has been found that the spectrometer is capable of reconstructing broad spectral features of a range of bandpass infrared filters between wavelengths of 4 and 12 mu m as well as identifying absorption features as narrow as 0.3 mu m in the IR spectrum of a polyethylene sheet

Demonstration of Bias-Controlled Algorithmic Tuning of Quantum Dots in a Well (DWELL) MidIR Detectors

The quantum-confined Stark effect in intersublevel transitions present in quantum-dots-in-a-well (DWELL) detectors gives rise to a midIR spectral response that is dependent upon the detector\u27s operational bias. The spectral responses resulting from different biases exhibit spectral shifts, albeit with significant spectral overlap. A postprocessing algorithm was developed by Sakoglu that exploited this bias-dependent spectral diversity to predict the continuous and arbitrary tunability of the DWELL detector within certain limits. This paper focuses on the experimental demonstration of the DWELL-based spectral tuning algorithm. It is shown experimentally that it is possible to reconstruct the spectral content of a target electronically without using any dispersive optical elements for tuning, thereby demonstrating a DWELL-based algorithmic spectrometer. The effects of dark current, detector temperature, and bias selection on the tuning capability are also investigated experimentally

Wwox deletion leads to reduced GABA-ergic inhibitory interneuron numbers and activation of microglia and astrocytes in mouse hippocampus

The association of WW domain-containing oxidoreductase WWOX gene loss of function with central nervous system (CNS) related pathologies is well documented. These include spinocerebellar ataxia, epilepsy and mental retardation (SCAR12, OMIM: 614322) and early infantile epileptic encephalopathy (EIEE28, OMIM: 616211) syndromes. However, there is complete lack of understanding of the pathophysiological mechanisms at play. In this study, using a Wwox knockout (Wwox KO) mouse model (2 weeks old, both sexes) and stereological studies we observe that Wwox deletion leads to a significant reduction in the number of hippocampal GABA-ergic (γ-aminobutyric acid) interneurons. Wwox KO mice displayed significantly reduced numbers of calcium-binding protein parvalbumin (PV) and neuropeptide Y (NPY) expressing interneurons in different subfields of the hippocampus in comparison to Wwox wild-type (WT) mice. We also detected decreased levels of Glutamic Acid Decarboxylase protein isoforms GAD65/67 expression in Wwox null hippocampi suggesting lower levels of GABA synthesis. In addition, Wwox deficiency was associated with signs of neuroinflammation such as evidence of activated microglia, astrogliosis, and overexpression of inflammatory cytokines Tnf-a and Il6. We also performed comparative transcriptome-wide expression analyses of neural stem cells grown as neurospheres from hippocampi of Wwox KO and WT mice thus identifying 283 genes significantly dysregulated in their expression. Functional annotation of transcriptome profiling differences identified ?neurological disease? and ?CNS development related functions? to be significantly enriched. Several epilepsy-related genes were found differentially expressed in Wwox KO neurospheres. This study provides the first genotype-phenotype observations as well as potential mechanistic clues associated with Wwox loss of function in the brain.Fil: Hussain, Tabish. University of Texas Health Science Center at Houston. University of Texas Md Anderson Cancer Center; Estados UnidosFil: Kil, Hyunsuk. University of Texas Health Science Center at Houston. University of Texas Md Anderson Cancer Center; Estados UnidosFil: Hattiangady, Bharathi. Texas A&M Health Science Center College of Medicine; Estados UnidosFil: Lee, Jaeho. University of Texas Health Science Center at Houston. University of Texas Md Anderson Cancer Center; Estados UnidosFil: Kodali, Maheedhar. Texas A&M Health Science Center College of Medicine; Estados UnidosFil: Shuai, Bing. Texas A&M Health Science Center College of Medicine; Estados UnidosFil: Attaluri, Sahithi. Texas A&M Health Science Center College of Medicine; Estados UnidosFil: Takata, Yoko. University of Texas Health Science Center at Houston. University of Texas Md Anderson Cancer Center; Estados UnidosFil: Shen, Jianjun. University of Texas Health Science Center at Houston. University of Texas Md Anderson Cancer Center; Estados UnidosFil: Abba, Martín Carlos. Universidad Nacional de La Plata. Facultad de Ciencias Médicas. Centro de Investigaciones Inmunológicas Básicas y Aplicadas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Shetty, Ashok K.. Texas A&M Health Science Center College of Medicine; Estados UnidosFil: Aldaz, Claudio Marcelo. University of Texas Health Science Center at Houston. University of Texas Md Anderson Cancer Center; Estados Unido

Growth and optimization of quantum dots-in-a-well infrared photodetectors

Quantum dot infrared photodetectors (QDIPs) have been shown to be a key technology in mid and long wavelength (3-14 μm) infrared detection due to their potential for normal incidence operation and low dark current. In our research group, we have been investigating infrared detectors based on intersubband transitions in a novel InAs/In0.15Ga0.85As quantum dots-in-well (DWELL) heterostructure. In the DWELL structure, the InAs quantum dots are placed in an In0.15Ga0.85As well, which in turn is placed in a GaAs matrix. Due to the large band offset between the ground electronic state of the InAs quantum dot and conduction band edge of the GaAs barrier, thermionic emission and dark current are significantly reduced in the DWELL structure. The DWELL design also offers other advantages such as better control over the operating peak response wavelength and bias dependent tunable spectral response based on the quantum confined stark effect (QCSE). We have recently fabricated the first long wavelength quantum dot infrared photodetector (QDIP) focal plane array based on this system and for the first time collobarators at Jet Propulsion Laboratory (JPL) have shown that QDIP performance has surpassed that of Quantum Well Infrared Photodetector (QWIP). In this work, we will investigate various methods we implemented in improving the performance of the DWELL photodetectors. Although QDIPs based on intersubband transitions have been investigated before, there has been no careful study on the effects of Si-doping on the performance of these detectors. A careful study has been done to determine the optimal doping of the InAs/In0.15Ga0.85As/GaAs DWELL detectors. It has been found that 3 x 1010 cm-2 is the optimal doping for the DWELL detectors. It has been observed that the spectral response, photocurrent, dark current, responsivity and detectivity (D*) increased with the amount of doping in the InAs QDs. In addition, the background limited infrared photodetector (BLIP) temperature (91K) is the highest for one electron per dot sample. In our standard QDIPs there is only a single pass of incident light through the active region. The development of a mechanism for multiple light passes through the active region should result in a significant responsivity enhancement of QDIP detectors. One such method to create multiple light passes is to add a mirror that reflects the light back into the active region effectively developing an optically resonant cavity. In this work, we have epitaxially inserted a DBR below the QDIP device that has a broad reflectivity spectrum (i.e. 8-11μm) and designed the resonant cavity for 9.5 μm wavelength. We have observed an increase in the responsivity of the device (0.76A/W at 1.4V) relative to devices with the same active region and no mirror or cavity. Hence, we believe that the QDIP with resonant cavity and distributed Bragg reflector has improved the performance of the device. The D* increased by a factor of three compared to the standard DWELL at a bias of 1.2 V and 77 K. In the standard QDIP the average compressive strain in the DWELL is about 1.35% and, therefore, more number of DWELLs cannot be grown without introducing defects or dislocations. Ideally, more number of DWELLs mean more absorption, which translates to increased quantum efficiency and performance of the device. A low strain alternative design InAs/GaAs/Al0.1Ga0.9As DWELL structure is developed which maintains approximately the same band offset between the singly degenerate ground state of the dot and the conduction band edge of the barrier. This alternative design has only (~0.35%) compressive strain in the DWELL, which allows incorporation of more DWELL layers in the active region. We observed spectrally tunable response with bias and long wave IR response at 6.2 μm and 8.4 μm. This design was also tech transferred to JPL who demonstrated a 640 x 512 infrared camera with 40 mK NEDT at 60 K. Further work is being done to fabricate FPA based on this device and compare it with the standard DWELL design

Plasmon assisted photonic crystal quantum dot sensors

We report Quantum Dot Infrared Detectors (QDIP) where light coupling to the self assembled quantum dots is achieved through plasmons occurring at the metal-semiconductor interface. The detector structure consists of an asymmetric InAs/InGaAs/GaAs dots-in-a-well (DWELL) structure and a thick layer of GaAs sandwiched between two highly doped n-GaAs contact layers, grown on a semi-insulating GaAs substrate. The aperture of the detector is covered with a thin metallic layer which along with the dielectric layer confines light in the vertical direction. Sub-wavelength two-dimensional periodic patterns etched in the metallic layer covering the aperture of the detector and the active region creates a micro-cavity that concentrate light in the active region leading to intersubband transitions between states in the dot and the ones in the well. The sidewalls of the detector were also covered with metal to ensure that there is no leakage of light into the active region other than through the metal covered aperture. An enhanced spectral response when compared to the normal DWELL detector is obtained despite the absence of any aperture in the detector. The spectral response measurements show that the Long Wave InfraRed (LWIR) region is enhanced when compared to the Mid Wave InfraRed (MWIR) region. This may be due to coupling of light into the active region by plasmons that are excited at the metal-semiconductor interface. The patterned metal-dielectric layers act as an optical resonator thereby enhancing the coupling efficiency of light into the active region at the specified frequency. The concept of plasmon-assisted coupling is in principle technology agnostic and can be easily integrated into present day infrared sensors

Just-in-Time Access for Databases: Harnessing AI for Smarter, Safer Permissions

In the evolving landscape of data security, traditional access control mechanisms often fall short in addressing dynamic and context-specific requirements. As data breaches become more sophisticated, organizations require more adaptive and intelligent access control strategies. This paper explores the integration of Artificial Intelligence (AI) into Just-in-Time (JIT) access control models to enhance database security. By leveraging AI, we aim to create adaptive, context-aware permission systems that grant access precisely when needed, reducing the attack surface and mitigating unauthorized access risks. We begin by analyzing traditional access control methods such as Role-Based Access Control (RBAC), Mandatory Access Control (MAC), and Discretionary Access Control (DAC), highlighting their limitations in handling real-time access control scenarios. The paper then explores the benefits of JIT access control, emphasizing how it minimizes over-privileged accounts and reduces security vulnerabilities. Furthermore, we investigate the role of AI in cybersecurity, particularly in real-time monitoring, anomaly detection, and decision-making processes for access control. To achieve a more intelligent access control framework, we propose an AI-driven JIT access model that incorporates machine learning algorithms, user behavior analytics, and contextual evaluation to determine access permissions dynamically. The proposed model is evaluated through simulations and case studies to measure its effectiveness in preventing unauthorized access, reducing the attack surface, and enhancing overall database security. The results demonstrate that our AI-driven approach significantly improves access accuracy, minimizes false positives and negatives, and optimizes response times compared to traditional methods. Through comprehensive analysis, this research provides a roadmap for organizations looking to implement AIenhanced JIT access control mechanisms. By dynamically granting access based on real-time behavioral and contextual assessments, organizations can significantly improve database security, reduce administrative overhead, and mitigate insider threats. Future research directions include further refinement of AI models, integration with multi-factor authentication mechanisms, and testing across diverse real-world scenarios to enhance security effectiveness

Sustainable Access Management for Cloud Instances With SSH Securing Cloud Infrastructure With PAM Solutions

In the era of cloud computing, securing access to cloud instances is paramount for protecting sensitive data and ensuring compliance with industry standards. Secure Shell (SSH) access management plays a critical role in safeguarding cloud environments by controlling how users interact with cloud-based systems. However, traditional SSH access management methods can often be cumbersome, error-prone, and vulnerable to breaches. This paper explores the implementation of Privileged Access Management (PAM) solutions for enhancing the security of SSH access to cloud instances. By integrating PAM systems with cloud infrastructure, organizations can enforce stricter access controls, enable detailed session auditing, and reduce the risk of unauthorized access. The paper discusses best practices for managing SSH keys, monitoring user activity, and automating access management workflows using PAM solutions, ultimately improving the security posture of cloud environments