2022 roadmap on neuromorphic computing and engineering

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10$^{18}$ calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community

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

Organic electrochemical networks for biocompatible and implantable machine learning: Organic bioelectronic beyond sensing

How can the brain be such a good computer? Part of the answer lies in the astonishing number of neurons and synapses that process electrical impulses in parallel. Part of it must be found in the ability of the nervous system to evolve in response to external stimuli and grow, sharpen, and depress synaptic connections. However, we are far from understanding even the basic mechanisms that allow us to think, be aware, recognize patterns, and imagine. The brain can do all this while consuming only around 20 Watts, out-competing any human-made processor in terms of energy-efficiency. This question is of particular interest in a historical era and technological stage where phrases like machine learning and artificial intelligence are more and more widespread, thanks to recent advances produced in the field of computer science. However, brain-inspired computation is today still relying on algorithms that run on traditional silicon-made, digital processors. Instead, the making of brain-like hardware, where the substrate itself can be used for computation and it can dynamically update its electrical pathways, is still challenging. In this work, I tried to employ organic semiconductors that work in electrolytic solutions, called organic mixed ionic-electronic conductors (OMIECs) to build hardware capable of computation. Moreover, by exploiting an electropolymerization technique, I could form conducting connections in response to electrical spikes, in analogy to how synapses evolve when the neuron fires. After demonstrating artificial synapses as a potential building block for neuromorphic chips, I shifted my attention to the implementation of such synapses in fully operational networks. In doing so, I borrowed the mathematical framework of a machine learning approach known as reservoir computing, which allows computation with random (neural) networks. I capitalized my work on demonstrating the possibility of using such networks in-vivo for the recognition and classification of dangerous and healthy heartbeats. This is the first demonstration of machine learning carried out in a biological environment with a biocompatible substrate. The implications of this technology are straightforward: a constant monitoring of biological signals and fluids accompanied by an active recognition of the presence of malign patterns may lead to a timely, targeted and early diagnosis of potentially mortal conditions. Finally, in the attempt to simulate the random neural networks, I faced difficulties in the modeling of the devices with the state-of-the-art approach. Therefore, I tried to explore a new way to describe OMIECs and OMIECs-based devices, starting from thermodynamic axioms. The results of this model shine a light on the mechanism behind the operation of the organic electrochemical transistors, revealing the importance of the entropy of mixing and suggesting new pathways for device optimization for targeted applications

Scalable and high-sensitivity readout of silicon quantum devices

Quantum computing is predicted to provide unprecedented enhancements in computational power. A quantum computer requires implementation of a well-defined and controlled quantum system of many interconnected qubits, each defined using fragile quantum states. The interest in a spin-based quantum computer in silicon stems from demonstrations of very long spin-coherence times, high-fidelity single spin control and compatibility with industrial mass-fabrication. Industrial scale fabrication of the silicon platform offers a clear route towards a large-scale quantum computer, however, some of the processes and techniques employed in qubit demonstrators are incompatible with a dense and foundry-fabricated architecture. In particular, spin-readout utilises external sensors that require nearly the same footprint as qubit devices. In this thesis, improved readout techniques for silicon quantum devices are presented and routes towards implementation of a scalable and high-sensitivity readout architecture are investigated. Firstly, readout sensitivity of compact gate-based sensors is improved using a high-quality factor resonator and Josephson parametric amplifier that are fabricated separately from quantum dots. Secondly, an integrated transistor-based control circuit is presented using which sequential readout of two quantum dot devices using the same gate-based sensor is achieved. Finally, a large-scale readout architecture based on random-access and frequency multiplexing is introduced. The impact of readout circuit footprint on readout sensitivity is determined, showing routes towards integration of conventional circuits with quantum devices in a dense architecture, and a fault-tolerant architecture based on mediated exchange is introduced, capable of relaxing the limitations on available control circuit footprint per qubit. Demonstrations are based on foundry-fabricated transistors and few-electron quantum dots, showing that industry fabrication is a viable route towards quantum computation at a scale large enough to begin addressing the most challenging computational problems

Gate-based sensing of silicon quantum dot devices towards 2D scaling

This thesis focuses on using the radio-frequency reflectometry technique for dispersive gate sensing of foundry fabricated silicon nanowire quantum dot devices. I will attempt to answer three questions relating to the scalability of these devices. How do electron and hole spin qubits perform in silicon quantum dots? How do we implement and distribute the placement of dispersive gate sensors in scaled-up quantum dot arrays? And how does a single dopant in the silicon channel affect the gate-defined quantum dot? First, I investigate the difference between electron and hole quantum dots in an ambipolar nanowire device which successfully demonstrated reconfigurable single and double electron and hole quantum dots in the same crystalline environment. I further investigate the effective bath temperature of two-dimensional electron gas and two-dimensional hole gas by performing the thermometry experiment on the same type of device. Secondly, I demonstrate a two-dimensional quantum dot array enabled by a floating gate architecture between silicon nanowires. An analytical model is developed to study the capacitive coupling between remote quantum dots over different distances. Coupling strength under different qubit encodings is also discussed to show the best implementation for neighbour silicon nanowires. Finally, the in-situ dispersive gate sensing allows the measurement of the inter-dot transition between the bismuth donor-dot system. The novel implementation with bismuth donor can open up the possibility of a hybrid singlet-triplet qubit or transferring a coherent spin state between the quantum dot and the donor

Improving the Readout of Semiconducting Qubits

Semiconducting qubits are a promising platform for quantum computers. In particular, silicon spin qubits have made a number of advancements recently including long coherence times, high-fidelity single-qubit gates, two-qubit gates, and high-fidelity readout. However, all operations likely require improvement in fidelity and speed, if possible, to realize a quantum computer. Readout fidelity and speed, in general, are limited by circuit challenges centered on extracting low signal from a device in a dilution refrigerator connected to room temperature amplifiers by long coaxial cables with relatively high capacitance. Readout fidelity specifically is limited by the time it takes to reliably distinguish qubit states relative to the characteristic decay time of the excited state, T1. This dissertation explores the use of heterojunction bipolar transistor (HBT) circuits to amplify the readout signal of silicon spin qubits at cryogenic temperatures. The cryogenic amplification approach has numerous advantages including low implementation overhead, low power relative to the available cooling power, and high signal gain at the mixing chamber stage leading to around a factor of ten speedup in readout time for a similar signal-to-noise ratio. The faster readout time generally increases fidelity, since it is much faster than the T1 time. Two HBT amplification circuits have been designed and characterized. One design is a low-power, base-current biased configuration with non-linear gain (CB-HBT), and the second is a linear-gain, AC-coupled configuration (AC-HBT). They can operate at powers of 1 and 10 μW, respectfully, and not significantly heat electrons. The noise spectral density referred to the input for both circuits is around 15 to 30 fA/√Hz, which is low compared to previous cases such as the dual-stage, AC-coupled HEMT circuit at ~ 70 fA/√Hz. Both circuits achieve charge sensitivity between 300 and 400 μe/√Hz, which approaches the best alternatives (e.g., RF-SET at ~ 140 μe/√Hz) but with much less implementation overhead. For the single-shot latched charge readout performed, both circuits achieve high-fidelity readout in times \u3c 10 μs with bit error rates \u3c 10-3, which is a great improvement over previous work at \u3e 70 μs. The readout speed-up in principle also reduces the production of errors due to excited state relaxation by a factor of ~ 10. All of these results are possible with relatively simple, low-power transistor circuits which can be mounted close to the qubit device at the mixing chamber stage of the dilution refrigerator

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Research and Technology 1990

A brief but comprehensive review is given of the technical accomplishments of the NASA Lewis Research Center during the past year. Topics covered include instrumentation and controls technology; internal fluid dynamics; aerospace materials, structures, propulsion, and electronics; space flight systems; cryogenic fluids; Space Station Freedom systems engineering, photovoltaic power module, electrical systems, and operations; and engineering and computational support