Accelerate & Actualize: Can 2D Materials Bridge the Gap Between Neuromorphic Hardware and the Human Brain?

Two-dimensional (2D) materials present an exciting opportunity for devices and systems beyond the von Neumann computing architecture paradigm due to their diversity of electronic structure, physical properties, and atomically-thin, van der Waals structures that enable ease of integration with conventional electronic materials and silicon-based hardware. All major classes of non-volatile memory (NVM) devices have been demonstrated using 2D materials, including their operation as synaptic devices for applications in neuromorphic computing hardware. Their atomically-thin structure, superior physical properties, i.e., mechanical strength, electrical and thermal conductivity, as well as gate-tunable electronic properties provide performance advantages and novel functionality in NVM devices and systems. However, device performance and variability as compared to incumbent materials and technology remain major concerns for real applications. Ultimately, the progress of 2D materials as a novel class of electronic materials and specifically their application in the area of neuromorphic electronics will depend on their scalable synthesis in thin-film form with desired crystal quality, defect density, and phase purity.Comment: Neuromorphic Computing, 2D Materials, Heterostructures, Emerging Memory Devices, Resistive, Phase-Change, Ferroelectric, Ferromagnetic, Crossbar Array, Machine Learning, Deep Learning, Spiking Neural Network

A Contribution Towards Intelligent Autonomous Sensors Based on Perovskite Solar Cells and Ta2O5/ZnO Thin Film Transistors

Many broad applications in the field of robotics, brain-machine interfaces, cognitive computing, image and speech processing and wearables require edge devices with very constrained power and hardware requirements that are challenging to realize. This is because these applications require sub-conscious awareness and require to be always “on”, especially when integrated with a sensor node that detects an event in the environment. Present day edge intelligent devices are typically based on hybrid CMOS-memristor arrays that have been so far designed for fast switching, typically in the range of nanoseconds, low energy consumption (typically in nano-Joules), high density and endurance (exceeding 1015 cycles). On the other hand, sensory-processing systems that have the same time constants and dynamics as their input signals, are best placed to learn or extract information from them. To meet this requirement, many applications are implemented using external “delay” in the memristor, in a process which enables each synapse to be modeled as a combination of a temporal delay and a spatial weight parameter. This thesis demonstrates a synaptic thin film transistor capable of inherent logic functions as well as compute-in-memory on similar time scales as biological events. Even beyond a conventional crossbar array architecture, we have relied on new concepts in reservoir computing to demonstrate a delay system reservoir with the highest learning efficiency of 95% reported to date, in comparison to equivalent two terminal memristors, using a single device for the task of image processing. The crux of our findings relied on enhancing our capability to model the unique physics of the device, in the scope of the current thesis, that is not amenable to conventional TCAD simulations. The model provides new insight into the redox characteristics of the gate current and paves way for assessment of device performance in compute-in-memory applications. The diffusion-based mechanism of the device, effectively enables time constants that have potential in applications such as gesture recognition and detection of cardiac arrythmia. The thesis also reports a new orientation of a solution processed perovskite solar cell with an efficiency of 14.9% that is easily integrable into an intelligent sensor node. We examine the influence of the growth orientation on film morphology and solar cell efficiency. Collectively, our work aids the development of more energy-efficient, powerful edge-computing sensor systems for upcoming applications of the IOT

Techniques of Energy-Efficient VLSI Chip Design for High-Performance Computing

How to implement quality computing with the limited power budget is the key factor to move very large scale integration (VLSI) chip design forward. This work introduces various techniques of low power VLSI design used for state of art computing. From the viewpoint of power supply, conventional in-chip voltage regulators based on analog blocks bring the large overhead of both power and area to computational chips. Motivated by this, a digital based switchable pin method to dynamically regulate power at low circuit cost has been proposed to make computing to be executed with a stable voltage supply. For one of the widely used and time consuming arithmetic units, multiplier, its operation in logarithmic domain shows an advantageous performance compared to that in binary domain considering computation latency, power and area. However, the introduced conversion error reduces the reliability of the following computation (e.g. multiplication and division.). In this work, a fast calibration method suppressing the conversion error and its VLSI implementation are proposed. The proposed logarithmic converter can be supplied by dc power to achieve fast conversion and clocked power to reduce the power dissipated during conversion. Going out of traditional computation methods and widely used static logic, neuron-like cell is also studied in this work. Using multiple input floating gate (MIFG) metal-oxide semiconductor field-effect transistor (MOSFET) based logic, a 32-bit, 16-operation arithmetic logic unit (ALU) with zipped decoding and a feedback loop is designed. The proposed ALU can reduce the switching power and has a strong driven-in capability due to coupling capacitors compared to static logic based ALU. Besides, recent neural computations bring serious challenges to digital VLSI implementation due to overload matrix multiplications and non-linear functions. An analog VLSI design which is compatible to external digital environment is proposed for the network of long short-term memory (LSTM). The entire analog based network computes much faster and has higher energy efficiency than the digital one

Ultrahigh drive current and large selectivity in GeS selector

Funder: National Key Research and Development Program of China (2017YFB0206101); Strategic Priority Research Program of the Chinese Academy of Sciences (XDB44010200); Hundred Talents Program (Chinese Academy of Sciences); Shanghai Pujiang Talent Program (18PJ1411100)Abstract: Selector devices are indispensable components of large-scale nonvolatile memory and neuromorphic array systems. Besides the conventional silicon transistor, two-terminal ovonic threshold switching device with much higher scalability is currently the most industrially favored selector technology. However, current ovonic threshold switching devices rely heavily on intricate control of material stoichiometry and generally suffer from toxic and complex dopants. Here, we report on a selector with a large drive current density of 34 MA cm−2 and a ~106 high nonlinearity, realized in an environment-friendly and earth-abundant sulfide binary semiconductor, GeS. Both experiments and first-principles calculations reveal Ge pyramid-dominated network and high density of near-valence band trap states in amorphous GeS. The high-drive current capacity is associated with the strong Ge-S covalency and the high nonlinearity could arise from the synergy of the mid-gap traps assisted electronic transition and local Ge-Ge chain growth as well as locally enhanced bond alignment under high electric field

The Fuzziness in Molecular, Supramolecular, and Systems Chemistry

Fuzzy Logic is a good model for the human ability to compute words. It is based on the theory of fuzzy set. A fuzzy set is different from a classical set because it breaks the Law of the Excluded Middle. In fact, an item may belong to a fuzzy set and its complement at the same time and with the same or different degree of membership. The degree of membership of an item in a fuzzy set can be any real number included between 0 and 1. This property enables us to deal with all those statements of which truths are a matter of degree. Fuzzy logic plays a relevant role in the field of Artificial Intelligence because it enables decision-making in complex situations, where there are many intertwined variables involved. Traditionally, fuzzy logic is implemented through software on a computer or, even better, through analog electronic circuits. Recently, the idea of using molecules and chemical reactions to process fuzzy logic has been promoted. In fact, the molecular word is fuzzy in its essence. The overlapping of quantum states, on the one hand, and the conformational heterogeneity of large molecules, on the other, enable context-specific functions to emerge in response to changing environmental conditions. Moreover, analog input–output relationships, involving not only electrical but also other physical and chemical variables can be exploited to build fuzzy logic systems. The development of “fuzzy chemical systems” is tracing a new path in the field of artificial intelligence. This new path shows that artificially intelligent systems can be implemented not only through software and electronic circuits but also through solutions of properly chosen chemical compounds. The design of chemical artificial intelligent systems and chemical robots promises to have a significant impact on science, medicine, economy, security, and wellbeing. Therefore, it is my great pleasure to announce a Special Issue of Molecules entitled “The Fuzziness in Molecular, Supramolecular, and Systems Chemistry.” All researchers who experience the Fuzziness of the molecular world or use Fuzzy logic to understand Chemical Complex Systems will be interested in this book

Electronic Nanodevices

The start of high-volume production of field-effect transistors with a feature size below 100 nm at the end of the 20th century signaled the transition from microelectronics to nanoelectronics. Since then, downscaling in the semiconductor industry has continued until the recent development of sub-10 nm technologies. The new phenomena and issues as well as the technological challenges of the fabrication and manipulation at the nanoscale have spurred an intense theoretical and experimental research activity. New device structures, operating principles, materials, and measurement techniques have emerged, and new approaches to electronic transport and device modeling have become necessary. Examples are the introduction of vertical MOSFETs in addition to the planar ones to enable the multi-gate approach as well as the development of new tunneling, high-electron mobility, and single-electron devices. The search for new materials such as nanowires, nanotubes, and 2D materials for the transistor channel, dielectrics, and interconnects has been part of the process. New electronic devices, often consisting of nanoscale heterojunctions, have been developed for light emission, transmission, and detection in optoelectronic and photonic systems, as well for new chemical, biological, and environmental sensors. This Special Issue focuses on the design, fabrication, modeling, and demonstration of nanodevices for electronic, optoelectronic, and sensing applications

Applications of artificial neural networks in three agro-environmental systems: microalgae production, nutritional characterization of soils and meteorological variables management

La agricultura es una actividad esencial para los humanos, es altamente dependiente de las condiciones meteorológicas y foco de investigación e innovación con el objetivo de enfrentar diversos desafíos. El cambio climático, calentamiento global y la degradación de los ecosistemas agrícolas son sólo algunos de los problemas que los humanos enfrentamos para continuar con la esencial producción de alimentos. Buscando la innovación en el sector agrícola, se consideraron tres tópicos principales de investigación para esta tesis; la producción de microalgas, el color del suelo y la fertilidad, y la adquisición de datos meteorológicos. Estos temas tienen roles cada vez más importantes en la agricultura, especialmente bajo la incertidumbre del futuro de la producción de alimentos. Las microalgas son una interesante alternativa para la fertilización de cultivos y la sostenibilidad del suelo; mientras que los parámetros de fertilidad del suelo necesitan ser más estudiados para desarrollar métodos de análisis de menor costo y más rápidos para ayudar al manejo. La agricultura, como actividad altamente dependiente del clima, necesita de datos meteorológicos para anticipar eventos, planificar y manejar los cultivos eficientemente. Estos temas se seleccionaron con el propósito de mejorar el estado actual de la técnica, proponer nuevas alternativas basadas, principalmente, en la aplicación de redes neuronales artificiales (ANN) como una manera novedosa de resolver los problemas y generar conocimiento de aplicación directa en sistemas de cultivos. El objetivo principal de esta tesis fue generar modelos de ANNs capaces de abordar problemas relacionados con la agricultura, como una alternativa a los métodos tradicionales y más costosos empleados en el manejo, análisis y adquisición de datos en los sistemas agrarios.Departamento de Ingeniería Agrícola y ForestalDoctorado en Ciencia e Ingeniería Agroalimentaria y de Biosistema