In this work, we investigate by means of atomistic density functional theory simulations the interaction between cortisol (the target molecule) and monolayer MoS2 (the substrate). The aim is to assess viable strategies for the non-enzymatic chemical sensing of cortisol. Metal doping of the sensing material could offer a way to improve the device response upon analyte adsorption, and could also enable novel and alternative detection mechanisms. For such reasons, we explore metal doping of MoS2 with Ni, Pd, and Pt, as these are metal elements commonly used in experiments. Then, we study the material response from the structural, electronic, and charge-transfer points of view. Based on our results, we propose two possible sensing mechanisms and device architectures: (i) a field-effect transistor, and (ii) an electrochemical sensor. In the former, Ni-doped MoS2 would act as the FET channel, and the sensing mechanism involves the variation of the surface electrostatic charge upon the adsorption of cortisol. In the latter, MoS2 decorated with Pt nanoparticles could act as the working electrode, and the sensing mechanism would involve the reduction of cortisol. In addition, our findings may suggest the suitability of both doped and metal-doped MoS2 as sensing layers in an optical sensor.
The increasing amount of data to be processed on edge devices, such as cameras, has motivated Artificial Intelligence (AI) integration at the edge. Typical image processing methods performed at the edge, such as feature extraction or edge detection, use convolutional filters that are energy, computation, and memory hungry algorithms. But edge devices and cameras have scarce computational resources, bandwidth, and power and are limited due to privacy constraints to send data over to the cloud. Thus, there is a need to process image data at the edge. Over the years, this need has incited a lot of interest in implementing neuromorphic computing at the edge. Neuromorphic systems aim to emulate the biological neural functions to achieve energy-efficient computing. Recently, Oscillatory Neural Networks (ONN) present a novel brain-inspired computing approach by emulating brain oscillations to perform autoassociative memory types of applications. To speed up image edge detection and reduce its power consumption, we perform an in-depth investigation with ONNs. We propose a novel image processing method by using ONNs as a hetero-associative memory (HAM) for image edge detection. We simulate our ONN-HAM solution using first, a Matlab emulator, and then a fully digital ONN design. We show results on gray scale square evaluation maps, also on black and white and gray scale 28x28 MNIST images and finally on black and white 512x512 standard test images. We compare our solution with standard edge detection filters such as Sobel and Canny. Finally, using the fully digital design simulation results, we report on timing and resource characteristics, and evaluate its feasibility for real-time image processing applications. Our digital ONN-HAM solution can process images with up to 120x120 pixels (166 MHz system frequency) respecting real-time camera constraints. This work is the first to explore ONNs as hetero-associative memory for image processing applications.
In this paper, we assess an innovative concept of emulating biological neurons with oscillators to implement an oscillatory neural network (ONN) with beyond-CMOS devices based on vanadium dioxide (VO2). ONNs can be of interest as an ultra-low-power neuromorphic architecture capable of performing associative memory tasks, such as pattern recognition in IoT edge devices. To explore the benefits and costs of beyond-CMOS ONNs necessitates modeling, simulation, and design methods spanning from materials (e.g., atomistic methods) to devices (e.g., technology-computer-aided-design, TCAD) up to circuits (e.g., mixed-mode simulation, compact modeling). In this work, we report on the development of such an advanced design toolbox and the results on performance and features of beyond-CMOS ONNs. The proposed design toolbox allows exploring ONN scalability, accuracy, energy, and performance for pattern recognition applications.
Nano-oscillators based on phase-transition materials are being explored for the implementation of different non-conventional computing paradigms. In particular, vanadium dioxide (VO 2 ) devices are used to design autonomous non-linear oscillators from which oscillatory neural networks (ONNs) can be developed. In this work, we propose a new architecture for ONNs in which sub-harmonic injection locking (SHIL) is exploited to ensure that the phase information encoded in each neuron can only take two values. In this sense, the implementation of ONNs from neurons that inherently encode information with two-phase values has advantages in terms of robustness and tolerance to variability present in VO 2 devices. Unlike conventional interconnection schemes, in which the sign of the weights is coded in the value of the resistances, in our proposal the negative (positive) weights are coded using static inverting (non-inverting) logic at the output of the oscillator. The operation of the proposed architecture is shown for pattern recognition applications.
The collective behavior of many coupled oscillator systems is currently being explored for the implementation of different non-conventional computing paradigms. In particular, VO2 based nano-oscillators have been proposed to implement oscillatory neural networks that can serve as associative memories, useful in pattern recognition applications. Although the dynamics of a pair of coupled oscillators have already been extensively analyzed, in this paper, the topic is addressed more practically. Firstly, for the application mentioned above, each oscillator needs to be initialized in a given phase to represent the input pattern. We demonstrate the impact of this initialization mechanism on the final phase relationship of the oscillators. Secondly, such oscillatory networks are based on frequency synchronization, in which the impact of variability is critical. We carried out a comprehensive mathematical analysis of a pair of coupled oscillators taking into account both issues, which is a first step towards the design of the oscillatory neural networks for associative memory applications.