Roadmap on semiconductor-cell biointerfaces.

This roadmap outlines the role semiconductor-based materials play in understanding the complex biophysical dynamics at multiple length scales, as well as the design and implementation of next-generation electronic, optoelectronic, and mechanical devices for biointerfaces. The roadmap emphasizes the advantages of semiconductor building blocks in interfacing, monitoring, and manipulating the activity of biological components, and discusses the possibility of using active semiconductor-cell interfaces for discovering new signaling processes in the biological world

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

Prognostics and health management of power electronics

Prognostics and health management (PHM) is a major tool enabling systems to evaluate their reliability in real-time operation. Despite ground-breaking advances in most engineering and scientific disciplines during the past decades, reliability engineering has not seen significant breakthroughs or noticeable advances. Therefore, self-awareness of the embedded system is also often required in the sense that the system should be able to assess its own health state and failure records, and those of its main components, and take action appropriately. This thesis presents a radically new prognostics approach to reliable system design that will revolutionise complex power electronic systems with robust prognostics capability enhanced Insulated Gate Bipolar Transistors (IGBT) in applications where reliability is significantly challenging and critical. The IGBT is considered as one of the components that is mainly damaged in converters and experiences a number of failure mechanisms, such as bond wire lift off, die attached solder crack, loose gate control voltage, etc. The resulting effects mentioned are complex. For instance, solder crack growth results in increasing the IGBT’s thermal junction which becomes a source of heat turns to wire bond lift off. As a result, the indication of this failure can be seen often in increasing on-state resistance relating to the voltage drop between on-state collector-emitter. On the other hand, hot carrier injection is increased due to electrical stress. Additionally, IGBTs are components that mainly work under high stress, temperature and power consumptions due to the higher range of load that these devices need to switch. This accelerates the degradation mechanism in the power switches in discrete fashion till reaches failure state which fail after several hundred cycles. To this end, exploiting failure mechanism knowledge of IGBTs and identifying failure parameter indication are background information of developing failure model and prognostics algorithm to calculate remaining useful life (RUL) along with ±10% confidence bounds. A number of various prognostics models have been developed for forecasting time to failure of IGBTs and the performance of the presented estimation models has been evaluated based on two different evaluation metrics. The results show significant improvement in health monitoring capability for power switches.Furthermore, the reliability of the power switch was calculated and conducted to fully describe health state of the converter and reconfigure the control parameter using adaptive algorithm under degradation and load mission limitation. As a result, the life expectancy of devices has been increased. These all allow condition-monitoring facilities to minimise stress levels and predict future failure which greatly reduces the likelihood of power switch failures in the first place

A walk on the frontier of energy electronics with power ultra-wide bandgap oxides and ultra-thin neuromorphic 2D materials

Altres ajuts: the ICN2 is funded also by the CERCA programme / Generalitat de CatalunyaUltra-wide bandgap (UWBG) semiconductors and ultra-thin two-dimensional materials (2D) are at the very frontier of the electronics for energy management or energy electronics. A new generation of UWBG semiconductors will open new territories for higher power rated power electronics and deeper ultraviolet optoelectronics. Gallium oxide - GaO(4.5-4.9 eV), has recently emerged as a suitable platform for extending the limits which are set by conventional (-3 eV) WBG e.g. SiC and GaN and transparent conductive oxides (TCO) e.g. In2O3, ZnO, SnO2. Besides, GaO, the first efficient oxide semiconductor for energy electronics, is opening the door to many more semiconductor oxides (indeed, the largest family of UWBGs) to be investigated. Among these new power electronic materials, ZnGa2O4 (-5 eV) enables bipolar energy electronics, based on a spinel chemistry, for the first time. In the lower power rating end, power consumption also is also a main issue for modern computers and supercomputers. With the predicted end of the Moores law, the memory wall and the heat wall, new electronics materials and new computing paradigms are required to balance the big data (information) and energy requirements, just as the human brain does. Atomically thin 2D-materials, and the rich associated material systems (e.g. graphene (metal), MoS2 (semiconductor) and h-BN (insulator)), have also attracted a lot of attention recently for beyond-silicon neuromorphic computing with record ultra-low power consumption. Thus, energy nanoelectronics based on UWBG and 2D materials are simultaneously extending the current frontiers of electronics and addressing the issue of electricity consumption, a central theme in the actions against climate chang

Impact of thermal material properties and local ion concentration on Ti/HfOx-based analog devices

The realization of adaptive oxides for neuromorphic computing hinges on repeatable and predicable analog changes of electrical resistance, which is fundamentally controlled by the materials in and around the device. This work focuses on filamentary memristors which exhibit a non-volatile change in resistance by modulating the concentration of oxygen vacancies within a small (filamentary) region of an otherwise insulating oxide layer in a metal-insulator-metal (MIM) stack. Under an applied electric field, these devices experience localized temperature rises over 1000 K on picosecond timescales, with drift, diffusion, and thermophoresis causing the migration of oxygen ions and oxygen vacancies. All three of these mechanisms have a strong dependence on temperature. Therefore, the management of the thermal field is crucial to successful implementation of these materials and devices. This dissertation independently establishes the impact of the substrate and electrode thermal conductivity both experimentally and computationally. For biologically realistic pulse widths, low substrate thermal conductivities led to increased resistance changes in RRAM devices. Furthermore, scanning thermal microscopy was used to compare the in-situ temperature rise of the top electrode directly above the filament with the estimated value from the model. This established a method to estimate the filament temperature during biasing with an accuracy ~30 K. Computational results demonstrated the temperature of the capping layer (between the oxide and the top electrode) had the greatest impact on the resistance change. Thus, a low thermal conductivity capping layer led to significantly higher resistance changes. Further work exploring the importance of the capping layer revealed that slightly higher initial oxygen concentrations (~2 - 3%) caused larger resistance changes compared to lower concentrations. In summary, this work establishes the importance of the thermal properties not only in contact with the filament, but also far away (substrate and electrodes) and establishes the importance of understanding the interplay between the filament and the capping layer to further improve the analog resistance change of filamentary RRAMs.Ph.D