Exploring Non-Steady-State Charge Transport Dynamics in Information Processing: Insights from Reservoir Computing

Exploring nonlinear chemical dynamic systems for information processing has emerged as a frontier in chemical and computational research, seeking to replicate the brain's neuromorphic and dynamic functionalities. We have extensively explored the information processing capabilities of a nonlinear chemical dynamic system through theoretical modeling by integrating a non-steady-state proton-coupled charge transport system into reservoir computing (RC) architecture. Our system demonstrated remarkable success in tasks such as waveform recognition, voice identification and chaos system prediction. More importantly, through a quantitative study, we revealed the key role of the alignment between the signal processing frequency of the RC and the characteristic time of the dynamics of the nonlinear system, which dictates the efficiency of RC task execution, the reservoir states and the memory capacity in information processing. The system's information processing frequency range was further modulated by the characteristic time of the dynamic system, resulting in an implementation akin to a 'chemically-tuned band-pass filter' for selective frequency processing. Our study thus elucidates the fundamental requirements and dynamic underpinnings of the non-steady-state charge transport dynamic system for RC, laying a foundational groundwork for the application of dynamic molecular devices for in-materia computing.Comment: 12 pages, 7 figures and 1 tabl

Fabrication and Simulation of Perovskite Solar Cells

Since the dawning of the industrial revolution, the world has had a need for mass energy production. In the 1950s silicon solar panels were invented. Silicon solar panels have been the main source of solar energy production. They have set the standard for power conversion efficiency for subsequent generations of photovoltaic technology. Solar panels utilize light’s ability to generate an electron hole pair. By creating a PN Junction in the photovoltaic semiconductor, the electron and hole are directed in opposing layers of the solar panel generating the electric current. Second generation solar panels utilized different thin film materials to fabricate solar panels. Materials such as Cadmium Telluride, Copper Indium Gallium Selenide, and amorphous silicon. This technology is now seen commercially available around the world. In the research community a third generation of solar panel technology is being developed. Perovskites are an emerging third generation solar panel technology. Perovskites’ power conversion efficiency have increased from 3.8% to 24.2% over the span of a decade. Perovskite crystals have desirable optical properties such has a high absorption coefficient, long carrier diffusion length, and high photoluminescence. The most prominent types of perovskites for solar cell research are organic metal halide perovskites. These perovskites utilize the desirable properties of organic electronics. Electrochemical techniques such as additives, catalysts, excess of particular chemicals, and variations in antisolvents impact the electronic properties of the perovskite crystal. The perovskite is however on layer of the device. Solar cell devices incorporate multiple layers. The materials for the electron transport layer, hole transport material, and choice of metal electrode have an impact on device performance and the current voltage relationship. Current silicon photovoltaic devices are more expensive than conventional fossil fuel. Modeling perovskite solar cells in a simulated environment is critical for data analytics, real fabrication behavior projection, and quantum mechanics of the semiconductor device. Photovoltaic semiconductors are diodes which produce a current when exposed to light. The ideality factor is a parameter which tells how closely a semiconductor behaves to an ideal diode. In an ideal diode, the only mechanism for hole electron recombination is direct bimolecular recombination. Because there are multiple mechanisms of recombination, there are no real devices with a perfect ideality factor. The types of recombination occurring within a device can be inferred by its ideality factor. In this research. Analyzing fabricated perovskite solar cells using their ideality factor can indicate which type of recombination is dominant in the device. The interaction between the perovskite crystal and transport layers is of high interest as differentials in energy level bands can hinder overall power conversion efficiency and act as a site for nonradiative recombination loss. In addition, the use of Machine Learning (ML) to research and predict the opto-electronic properties of perovskite can greatly accelerate the development of this technology. ML techniques such as Linear Regression (LR), Support Vector Regression (SVR), and Artificial Neural Networks (ANNs) can greatly improve the chemical processing and manufacturing techniques. Such tools used to improve this technology have major impacts for the further proliferation of solar energy on a national scale. These tools can also be used to optimize power conversion efficiency of perovskites, This optimization is critical for commercial use of perovskite solar panel technology. Various electrochemical and fabrication strategies are currently being researched in order to optimize power conversion efficiency and minimize energy loss. There are current results which suggest the addition of particular ions in the perovskite crystal have a positive impact on the power conversion efficiency. The qualities of the cell such as crystallinity, defects, and grain size play important roles in the electrical properties of the cell. Along with the quality of the perovskite crystal, its interfacing with the transport layers plays a critical role in the operation of the device. In this thesis, perovskite solar cells are fabricated and simulated to research their optoelectronic properties. The optoelectronic behavior of simulated solar cells is manipulated to match that or cells. By researching this new optoelectronic material in a virtual environment, applicability and plausibility are demonstrated. This legitimizes the continued research of this third-generation solar panel material

Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing

Successful materials innovations can transform society. However, materials research often involves long timelines and low success probabilities, dissuading investors who have expectations of shorter times from bench to business. A combination of emergent technologies could accelerate the pace of novel materials development by 10x or more, aligning the timelines of stakeholders (investors and researchers), markets, and the environment, while increasing return-on-investment. First, tool automation enables rapid experimental testing of candidate materials. Second, high-throughput computing (HPC) concentrates experimental bandwidth on promising compounds by predicting and inferring bulk, interface, and defect-related properties. Third, machine learning connects the former two, where experimental outputs automatically refine theory and help define next experiments. We describe state-of-the-art attempts to realize this vision and identify resource gaps. We posit that over the coming decade, this combination of tools will transform the way we perform materials research. There are considerable first-mover advantages at stake, especially for grand challenges in energy and related fields, including computing, healthcare, urbanization, water, food, and the environment.Comment: 22 pages, 3 figure

Image sensing with multilayer, nonlinear optical neural networks

Optical imaging is commonly used for both scientific and technological applications across industry and academia. In image sensing, a measurement, such as of an object's position, is performed by computational analysis of a digitized image. An emerging image-sensing paradigm breaks this delineation between data collection and analysis by designing optical components to perform not imaging, but encoding. By optically encoding images into a compressed, low-dimensional latent space suitable for efficient post-analysis, these image sensors can operate with fewer pixels and fewer photons, allowing higher-throughput, lower-latency operation. Optical neural networks (ONNs) offer a platform for processing data in the analog, optical domain. ONN-based sensors have however been limited to linear processing, but nonlinearity is a prerequisite for depth, and multilayer NNs significantly outperform shallow NNs on many tasks. Here, we realize a multilayer ONN pre-processor for image sensing, using a commercial image intensifier as a parallel optoelectronic, optical-to-optical nonlinear activation function. We demonstrate that the nonlinear ONN pre-processor can achieve compression ratios of up to 800:1 while still enabling high accuracy across several representative computer-vision tasks, including machine-vision benchmarks, flow-cytometry image classification, and identification of objects in real scenes. In all cases we find that the ONN's nonlinearity and depth allowed it to outperform a purely linear ONN encoder. Although our experiments are specialized to ONN sensors for incoherent-light images, alternative ONN platforms should facilitate a range of ONN sensors. These ONN sensors may surpass conventional sensors by pre-processing optical information in spatial, temporal, and/or spectral dimensions, potentially with coherent and quantum qualities, all natively in the optical domain

Center for Space Microelectronics Technology

The 1991 Technical Report of the Jet Propulsion Laboratory Center for Space Microelectronics Technology summarizes the technical accomplishments, publications, presentations, and patents of the Center during the past year. The report lists 193 publications, 211 presentations, and 125 new technology reports and patents

Study of propagation and detection methods of terahertz radiation for spectroscopy and imaging

The applications of terahertz (THz, 1 THz is 1012 cycles per second or 300 pm in wavelength) radiation are rapidly expanding. In particular, THz imaging is emerging as a powerful technique to spatially map a wide variety of objects with spectral features which are present for many materials in THz region. Objects buried within dielectric structures can also be imaged due to the transparency of most dielectrics in this regime. Unfortunately, the image quality in such applications is inherently influenced by the scattering introduced by the sample inhomogeneities and by the presence of barriers that reduces both the transmitted power and the spatial resolution in particular frequency components. For continued development in THz radiation imaging, a comprehensive understanding of the role of these factors on THz radiation propagation and detection is vital. This dissertation focuses on the various aspects like scattering, attenuation, frequency filtering and waveguide propagation of THz radiation and its subsequent application to a stand-off THz interferometric imager under development. Using THz Time Domain spectroscopic set-up, the effect of scattering, guided THz propagation with loss and dispersion profile of hollow-core waveguides and various filtering structures are investigated. Interferometric detection scheme and subsequent agent identification is studied in detail using extensive simulation and modeling of various imaging system parameters

The development of fully automated RULA assessment system based on Computer Vision

The purpose of this study was to develop an automated, RULA-based posture assessment system using a deep learning algorithm to estimate RULA scores, including scores for wrist posture, based on images of workplace postures. The proposed posture estimation system reported a mean absolute error (MAE) of 2.86 on the validation dataset obtained by randomly splitting 20% of the original training dataset before data augmentation. The results of the proposed system were compared with those of two experts’ manual evaluation by computing the Intraclass correlation coefficient (ICC), which yielded index values greater than 0.75, thereby confirming good agreement between manual raters and the proposed system. This system will reduce the time required for postural evaluation while producing highly reliable RULA scores that are consistent with those generated by manual approach. Thus, we expect that this study will aid ergonomic experts in conducting RULA-based surveys of occupational postures in workplace conditions

Prognostics and health management of light emitting diodes

Prognostics is an engineering process of diagnosing, predicting the remaining useful life and estimating the reliability of systems and products. Prognostics and Health Management (PHM) has emerged in the last decade as one of the most efficient approaches in failure prevention, reliability estimation and remaining useful life predictions of various engineering systems and products. Light Emitting Diodes (LEDs) are optoelectronic micro-devices that are now replacing traditional incandescent and fluorescent lighting, as they have many advantages including higher reliability, greater energy efficiency, long life time and faster switching speed. Even though LEDs have high reliability and long life time, manufacturers and lighting systems designers still need to assess the reliability of LED lighting systems and the failures in the LED. This research provides both experimental and theoretical results that demonstrate the use of prognostics and health monitoring techniques for high power LEDs subjected to harsh operating conditions. Data driven, model driven and fusion prognostics approaches are developed to monitor and identify LED failures, based on the requirement for the light output power. The approaches adopted in this work are validated and can be used to assess the life of an LED lighting system after their deployment based on the power of the light output emitted. The data driven techniques are only based on monitoring selected operational and performance indicators using sensors whereas the model driven technique is based on sensor data as well as on a developed empirical model. Fusion approach is also developed using the data driven and the model driven approaches to the LED. Real-time implementation of developed approaches are also investigated and discussed

Devices and networks for optical switching

This thesis is concerned with some aspects of the application of optics to switching and computing. Two areas are dealt with: the design of switching networks which use optical interconnects, and the development and application of the t-SEED optical logic device. The work on optical interconnects looks at the multistage interconnection network which has been proposed as a hybrid switch using both electronics and optics. It is shown that the architecture can be mapped from one dimensional to two dimensional format, so that the machine makes full use of the space available to the optics. Other mapping rules are described which allow the network to make optimum use of the optical interconnects, and the endpoint is a hybrid optical-electronic machine which should be able to outperform an all-electronic equivalent. The development of the t-SEED optical logic device is described, which is the integration of a phototransistor with a multiple quantum well optical modulator. It is found to be important to have the modulator underneath rather than on top of the transistor to avoid unwanted thyristor action. In order for the transistor to have a high gain the collector must have a low doping level, the exit window in the substrate must be etched all the way to the emitter layer, and the etch must not damage the emitter-base junction. A real optical gain of 1.6 has been obtained, which is higher than has ever been reached before but is not as high as should be possible. Improvements to the device are suggested. A new model of the Fabry-Perot cavity is introduced which helps considerably in the interpretation of experimental measurements made on the quantum well modulators. Also a method of improving the contrast of the multiple quantum well modulator by grading the well widths is proposed which may find application in long wavelength transmission modulators. Some systems which make use of the t-SEED are considered. It is shown that the t-SEED device has the right characteristics for use as a neuron element in the optical implementation of a neural network. A new image processing network for clutter removal in binary images is introduced which uses the t-SEED, and a brief performance analysis suggests that the network may be superior to an all-electronic machine