Joanna Symonowicz

Bio: Joanna Symonowicz is an academic researcher from Vienna University of Technology. The author has contributed to research in topics: Materials science & Frame (networking). The author has an hindex of 1, co-authored 2 publications receiving 147 citations.

Ultrafast machine vision with 2D material neural network image sensors

Abstract: Machine vision technology has taken huge leaps in recent years, and is now becoming an integral part of various intelligent systems, including autonomous vehicles and robotics. Usually, visual information is captured by a frame-based camera, converted into a digital format and processed afterwards using a machine-learning algorithm such as an artificial neural network (ANN)1. The large amount of (mostly redundant) data passed through the entire signal chain, however, results in low frame rates and high power consumption. Various visual data preprocessing techniques have thus been developed2-7 to increase the efficiency of the subsequent signal processing in an ANN. Here we demonstrate that an image sensor can itself constitute an ANN that can simultaneously sense and process optical images without latency. Our device is based on a reconfigurable two-dimensional (2D) semiconductor8,9 photodiode10-12 array, and the synaptic weights of the network are stored in a continuously tunable photoresponsivity matrix. We demonstrate both supervised and unsupervised learning and train the sensor to classify and encode images that are optically projected onto the chip with a throughput of 20 million bins per second.

Fully Optical in Operando Investigation of Ambient Condition Electrical Switching in MoS2 Nanodevices

Abstract: MoS2 nanoswitches have shown superb ultralow switching energies without excessive leakage currents. However, the debate about the origin and volatility of electrical switching is unresolved due to the lack of adequate nanoimaging of devices in operando. Here, three optical techniques are combined to perform the first noninvasive in situ characterization of nanosized MoS2 devices. This study reveals volatile threshold resistive switching due to the intercalation of metallic atoms from electrodes directly between Mo and S atoms, without the assistance of sulfur vacancies. A “semi‐memristive” effect driven by an organic adlayer adjacent to MoS2 is observed, which suggests that nonvolatility can be achieved by careful interface engineering. These findings provide a crucial understanding of nanoprocess in vertically biased MoS2 nanosheets, which opens new routes to conscious engineering and optimization of 2D electronics.

In Operando Optical Tracking of Oxygen Vacancy Migration and Phase Change in few Nanometers Ferroelectric HZO Memories

Abstract: Ferroelectric materials offer a low‐energy, high‐speed alternative to conventional logic and memory circuitry. Hafnia‐based films have achieved single‐digit nm ferroelectricity, enabling further device miniaturization. However, they can exhibit nonideal behavior, specifically wake‐up and fatigue effects, leading to unpredictable performance variation over consecutive electronic switching cycles, preventing large‐scale commercialization. The origins are still under debate. Using plasmon‐enhanced spectroscopy, a non‐destructive technique sensitive to <1% oxygen vacancy variation, phase changes, and single switching cycle resolution, the first real‐time in operando nanoscale direct tracking of oxygen vacancy migration in 5 nm hafnium zirconium oxide during a pre‐wake‐up stage is provided. It is shown that the pre‐wake‐up leads to a structural phase change from monoclinic to orthorhombic phase, which further determines the device wake‐up. Further migration of oxygen ions in the phase changed material is then observed, producing device fatigue. These results provide a comprehensive explanation for the wake‐up and fatigue with Raman, photoluminescence and darkfield spectroscopy, combined with density functional theory and finite‐difference time‐domain simulations.

Real-time image processing with a 2D semiconductor neural network vision sensor

Abstract: In recent years, machine vision has taken huge leaps and is now becoming an integral part of various intelligent systems, including autonomous vehicles, robotics, and many others. Usually, visual information is captured by a frame-based camera, converted into a digital format, and processed afterwards using a machine learning algorithm such as an artificial neural network (ANN). A large amount of (mostly redundant) data being passed through the entire signal chain, however, results in low frame rates and large power consumption. Various visual data preprocessing techniques have thus been developed that allow to increase the efficiency of the subsequent signal processing in an ANN. Here, we demonstrate that an image sensor itself can constitute an ANN that is able to simultaneously sense and process optical images without latency. Our device is based on a reconfigurable two-dimensional (2D) semiconductor photodiode array, with the synaptic weights of the network being stored in a continuously tunable photoresponsivity matrix. We demonstrate both supervised and unsupervised learning and successfully train the sensor to classify and encode images, that are optically projected onto the chip, with a throughput of 20 million bins per second.

Two-dimensional materials for next-generation computing technologies.

Abstract: Rapid digital technology advancement has resulted in a tremendous increase in computing tasks imposing stringent energy efficiency and area efficiency requirements on next-generation computing. To meet the growing data-driven demand, in-memory computing and transistor-based computing have emerged as potent technologies for the implementation of matrix and logic computing. However, to fulfil the future computing requirements new materials are urgently needed to complement the existing Si complementary metal–oxide–semiconductor technology and new technologies must be developed to enable further diversification of electronics and their applications. The abundance and rich variety of electronic properties of two-dimensional materials have endowed them with the potential to enhance computing energy efficiency while enabling continued device downscaling to a feature size below 5 nm. In this Review, from the perspective of matrix and logic computing, we discuss the opportunities, progress and challenges of integrating two-dimensional materials with in-memory computing and transistor-based computing technologies. This Review discusses the recent progress and future prospects of two-dimensional materials for next-generation nanoelectronics.

Near-sensor and in-sensor computing

Abstract: The number of nodes typically used in sensory networks is growing rapidly, leading to large amounts of redundant data being exchanged between sensory terminals and computing units. To efficiently process such large amounts of data, and decrease power consumption, it is necessary to develop approaches to computing that operate close to or inside sensory networks, and that can reduce the redundant data movement between sensing and processing units. Here we examine the concept of near-sensor and in-sensor computing in which computation tasks are moved partly to the sensory terminals. We classify functions into low-level and high-level processing, and discuss the implementation of near-sensor and in-sensor computing for different physical sensing systems. We also analyse the existing challenges in the field and provide possible solutions for the hardware implementation of integrated sensing and processing units using advanced manufacturing technologies. This Perspective examines the concept of near-senor and in-sensor computing in which computation tasks are moved partly to the sensory terminals, exploring the challenges facing the field and providing possible solutions for the hardware implementation of integrated sensing and processing units using advanced manufacturing technologies.

Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit

Abstract: There is an ever-growing demand for artificial intelligence. Optical processors, which compute with photons instead of electrons, can fundamentally accelerate the development of artificial intelligence by offering substantially improved computing performance. There has been long-term interest in optically constructing the most widely used artificial-intelligence architecture, that is, artificial neural networks, to achieve brain-inspired information processing at the speed of light. However, owing to restrictions in design flexibility and the accumulation of system errors, existing processor architectures are not reconfigurable and have limited model complexity and experimental performance. Here, we propose the reconfigurable diffractive processing unit, an optoelectronic fused computing architecture based on the diffraction of light, which can support different neural networks and achieve a high model complexity with millions of neurons. Along with the developed adaptive training approach to circumvent system errors, we achieved excellent experimental accuracies for high-speed image and video recognition over benchmark datasets and a computing performance superior to that of cutting-edge electronic computing platforms. Linear diffractive structures are by themselves passive systems but researchers here exploit the non-linearity of a photodetector to realize a reconfigurable diffractive ‘processing’ unit. High-speed image and video recognition is demonstrated.

Highlighting photonics: looking into the next decade

Abstract: Let there be light–to change the world we want to be! Over the past several decades, and ever since the birth of the first laser, mankind has witnessed the development of the science of light, as light-based technologies have revolutionarily changed our lives. Needless to say, photonics has now penetrated into many aspects of science and technology, turning into an important and dynamically changing field of increasing interdisciplinary interest. In this inaugural issue of eLight, we highlight a few emerging trends in photonics that we think are likely to have major impact at least in the upcoming decade, spanning from integrated quantum photonics and quantum computing, through topological/non-Hermitian photonics and topological insulator lasers, to AI-empowered nanophotonics and photonic machine learning. This Perspective is by no means an attempt to summarize all the latest advances in photonics, yet we wish our subjective vision could fuel inspiration and foster excitement in scientific research especially for young researchers who love the science of light.

Transistors based on two-dimensional materials for future integrated circuits

Abstract: Field-effect transistors based on two-dimensional (2D) materials have the potential to be used in very large-scale integration (VLSI) technology, but whether they can be used at the front end of line or at the back end of line through monolithic or heterogeneous integration remains to be determined. To achieve this, multiple challenges must be overcome, including reducing the contact resistance, developing stable and controllable doping schemes, advancing mobility engineering and improving high-κ dielectric integration. The large-area growth of uniform 2D layers is also required to ensure low defect density, low device-to-device variation and clean interfaces. Here we review the development of 2D field-effect transistors for use in future VLSI technologies. We consider the key performance indicators for aggressively scaled 2D transistors and discuss how these should be extracted and reported. We also highlight potential applications of 2D transistors in conventional micro/nanoelectronics, neuromorphic computing, advanced sensing, data storage and future interconnect technologies. This Review examines the development of field-effect transistors based on two-dimensional materials and considers the challenges that need to be addressed for the devices to be incorporated into very large-scale integration (VLSI) technology.