Resistive switching refers to the controlled change in resistance of an electronic material, e.g. metal oxide, via the creation and modulation of nanoscale filaments. Although its physics is not yet fully understood, resistive switching devices (called memristors) are promising as efficient artificial synapses in neuro-inspired computing systems. However practical challenges exist. Current devices excel in only a few of the performance metrics necessary for circuit and system integration. Moreover, they exhibit non-idealities causing neuromorphic systems using these devices to have low performance. The project will address this key issue by pursuing device-system co-optimization across four objectives, aiming to engineer a single “hero” resistive switching technology with all the desired metrics. Aim 1 will develop resistive switching devices based on a new class of materials with broad compositional space, called high entropy oxides. Promising compositions will be fabricated in a high throughput fashion. In Aim 2, a proposed characterization method via a state-of-the-art mid-infrared laser will help understand in-operando the filamentary switching at nanoscale and uncover the physical mechanisms behind its non-idealities. The fabrication and characterization will iteratively target a broad range of performance metrics. Some metrics can only be quantified across a population of devices, so Aim 3 will integrate the optimized devices on transistor circuitry for benchmarking at scale. Aim 4 targets the applicability of these devices to next generation neuromorphic systems for machine learning training. Preliminary work on a multi-layer neural network validated this concept and indicated the need for co-optimization, as proposed. RobustNanoNet will address the interdisciplinary challenges towards a reliable resistive switching technology to support robust neuromorphic systems for energy efficient computing.

Building artificial neuronal networks (ANN) that mimic their biological prototypes is one of the remaining grand challenges in computing. Despite transistor scaling and improved architectures, modern supercomputers require kWs of power to replicate functionality for which living neural networks take only a few Ws. Currently there is no hardware that can provide a high synaptic density with reasonable energy consumption. Hybrid CMOS chips with stacked crossbars of analog non-volatile memory devices (NVM), like memristors, promise to deliver the required high density and connectivity. However, the crossbar architecture suffers from the sneak path problem, neighbouring devices creating electrical shorts around the selected device. Biological systems do not have this problem due to the inherent nonlinearity of their potentiation and spiking. A high nonlinearity can be reproduced in a crossbar by using a selector for each memory device. The selector commonly used is a transistor which limits the scalability and stackability. This project proposes an alternative selector based on a two-terminal MEMS switch. MEMS switches are heavily researched for RF applications, but their high nonlinearity makes them attractive as selectors for NVM. This project will design, simulate and fabricate a crossbar of integrated MEMS selectors and TiO2 memristor devices. Initially, the selectors and memristors will be optimized separately, then monolithically integrated. Their scalability and stackability will be investigated. Finally, a prototype 3x3 crossbar of integrated memristor/MEMS selectors will be fabricated and used to demonstrate vector matrix multiplication - a foundational element of many complex ANNs like a perceptron. The proposed work is not linked to a particular NVM (ReRAM is an example), being suitable for any dense crossbar system that requires selectors. The findings are relevant to the fields of hardware ANNs and non-volatile memories and to major industry players.

Multiple myeloma (MM) is an aggressive type of cancer of plasma cells where fast diagnosis and treatment monitoring is vital, especially given the overall low survival rate. Proteasome, a multi-catalytic complex essential in damaged protein degradation, has been recently established as a biomarker for MM, in which elevated levels have been observed in blood plasma of unhealthy individuals, as well as its impaired activity has been target for treatment alongside regular chemotherapy. PADMME addresses increasing contributions on proteasome electrochemical recognition and aims to develop novel interdisciplinary technologies translatable into POCT systems for MM care. For this, we propose the development of sensitive and selective dual-channel platforms comprising conductive polymeric fiber biosensors integrated on paper-based microfluidic supports to simultaneously detect quantity and specific activity of proteasome in blood plasma. Paper supports ensure disposability and ease of commercialization, whilst incorporation of microfluidic paths allows low sample volume requirements and in situ pre-treatment steps. Conductive polymeric fiber scaffolds provide increased signal resolution for the construction of biosensors, necessary for the recognition of low concentrations of the biomarker. Then, investigating proteasome at the dual-channel platform in drug-treated and untreated MM cell lines will demonstrate how changes in quantity and specific activity are specifically linked to the disease dynamics. The last step will comprise validation of the technology for analysis in blood plasma, technology able to improve overall monitoring of MM disease progression and therapeutic efficiency, crucial to overcome delays in treatment due to drug resistance complications. Training through research will allow me to refine my expertise, creativity and innovative potential on smart biosensor technologies translated into point-of-care testing devices for cancer care and drug assessment.

Multiple myeloma (MM) is an aggressive type of cancer of plasma cells where fast diagnosis and treatment monitoring is vital, especially given the overall low survival rate. Proteasome, a multi-catalytic complex essential in damaged protein degradation, has been recently established as a biomarker for MM, in which elevated levels have been observed in blood plasma of unhealthy individuals, as well as its impaired activity has been target for treatment alongside regular chemotherapy. PADMME addresses increasing contributions on proteasome electrochemical recognition and aims to develop novel interdisciplinary technologies translatable into POCT systems for MM care. For this, we propose the development of sensitive and selective dual-channel platforms comprising conductive polymeric fiber biosensors integrated on paper-based microfluidic supports to simultaneously detect quantity and specific activity of proteasome in blood plasma. Paper supports ensure disposability and ease of commercialization, whilst incorporation of microfluidic paths allows low sample volume requirements and in situ pre-treatment steps. Conductive polymeric fiber scaffolds provide increased signal resolution for the construction of biosensors, necessary for the recognition of low concentrations of the biomarker. Then, investigating proteasome at the dual-channel platform in drug-treated and untreated MM cell lines will demonstrate how changes in quantity and specific activity are specifically linked to the disease dynamics. The last step will comprise validation of the technology for analysis in blood plasma, technology able to improve overall monitoring of MM disease progression and therapeutic efficiency, crucial to overcome delays in treatment due to drug resistance complications. Training through research will allow me to refine my expertise, creativity and innovative potential on smart biosensor technologies translated into point-of-care testing devices for cancer care and drug assessment.