The electronics industry "ElecTech" sector is central to the UK's future economy, environment, and society. With over 1 million employees in sectors enabled by electronics, the contribution of electronic technologies is indispensable. At the heart of electronics are nanoelectronic semiconductor "chips", and it has a leading position in semiconductor intellectual property vendors and emerging areas such as quantum technologies, sustainable electronics manufacturing, and compound semiconductors. The UK's potential lies, and where its future role in the global semiconductor value chain lies, as evidenced in the BEIS committee inquiry. We will establish an Automated Nano AnaLysing, characterisatiOn and additive packaGing sUitE (ANALOGUE) suite. ANALOGUE will be an exemplary facility that provides a fully automated platform for semiconductor processing, from devices to applications, with centralised workflow design, data collection/capture and real-time analytics. ANALOGUE will enable wafer-scale fully automated electrical characterisation of devices including reliability and temperature cycling capabilities. A fully automated back-end processing platform is integrated enabling die- and wire-bonding, 3D printed electronics and additive heterogenous packaging, co-located with high-resolution printed circuit laser patterning. Co-located with the £35M James Watt Nanofabrication Centre (JWNC), and the Centre for Advanced Electronics (CAE), the facility will enable devices-to-systems across the ICT spectrum, towards a user-centric and responsible design approach for electronics manufacturing. With a team representing two application-oriented user groups, medical and industrial nanoelectronics, we will create an ecosystem whereby manufacturing, users, and circular economy experts are brought together as users of ANALOGUE. ANALOGUE will support research on implantables, wearables, and diagnostics, through ultrasonic devices. Embedding sustainable manufacturing and onshoring the research into the backend processes of electronics is crucial to meeting the requirements of future electronics design flows. Original Equipment Manufacturers (OEM) buyers like Apple are already demanding commitments from suppliers to decarbonise their products, with distributors expected to assess each product's environmental impact throughout its lifecycle - from design and manufacture to end-of-life. As such, ANALOGUE allows UK researchers to explore the "black-box" of the semiconductor supply chain using automated characterisation and heterogenous packaging, encompassed by an automation and data collection framework for evaluating the efficacy of our experimental workflows. ANALOGUE will be accessible to the UK's research community across HealthTech, Beyond-Moore Computing, and Circular and Sustainable Electronics. Owing to its automated and streamlined nature, ANALOGUE will allow users from different institutions to utilise the suite remotely, facilitated by expert technical support, enabling rapid innovation across the nanoelectronics spectrum, insulating the UK's electronics research eco-system from global supply chain interruptions, e.g. chip shortages, and underpinning new research into otherwise offshore aspects of the electronics manufacturing. ANALOGUE builds on the UK's internationally acknowledged strengths in low-power IC Design, electronic materials, and applications in sustainable manufacturing. The Glasgow collaboration as an essential link in the supply chain linking materials producers (e.g., IQE), designers (Arm) manufacturers (PragmatIC Semiconductors, Printed Electronics, MTC), with academic users. The ANALOGUE team will regularly engage with these stakeholders through joint projects, meetings, workshops, and targeted events. The alignment of the proposal with the strategic sustainable systems focus of UofG will also help the envisaged research's long-term planning and strategy building.

Advanced materials lie at the heart of a huge number of key modern technologies, from aerospace and automotive industries, to semiconductors through to surgical implants. The transmission electron microscope (TEM) is a key enabling technology for advanced material research because it offers two important pieces of atomic information: firstly the location of atoms can be determined from studies of elastic scattering of electrons by the sample, and secondly the chemical composition of atomic sites within the materials structure can be recovered from spectroscopic studies of the inelastic transfer of energy to the sample (either from direct energy loss or by the detection of characteristic X-rays). These two pieces of information underpin a huge research area exploring the relationship between materials microscopic structure and the macroscopic properties it exhibits. With the drive towards nanotechnologies and quantum devices the ability to discover the most precise understanding of individual atoms is an essential capability for facilities supporting research of advanced materials. The aim of the project is to develop, for the first time, an analytical TEM that not only offers cutting edge spectroscopy performance but which also is designed with artificial intelligence and automated workflows at its core. The first goal will be achieved through the incorporation of the latest generation of X-ray detectors and spectrometers to provide order of magnitude improvements in chemical sensitivity and precision. This capability is essential for the move to studying devices as small as a single atomic defect as well as for efficient analysis of large areas at atomic resolution. To achieve artificial intelligence (AI)-assisted experiments the project will tackle a number of technical challenges: i. Computer control of the TEM will be developed, allowing the computer to automatically adjust the sample stage and beam to address specific regions of interest and perform auto-tuning the experimental parameters to achieve detailed high resolution imaging and diffraction based analysis of nanometric regions without the need for continuous operator interaction. ii. The mechanism to identify regions of interest will utilise the full range of machine learning (ML) approaches to segment lower resolution data, which might come from fast large-area scanning in the TEM or be the result of ex-situ analysis by optical imaging, scanning probe microscopies, scanning electron microscopy or optical approaches to name but a few. iii AI training will allow the microscope control computer to build functional relationships between experimental results in the same way a human operator does, allowing faster and more systematic identification of novel features. Our proposed new smart automated TEM (AutomaTEM) offers the opportunity to gain at least an order of magnitude increase in the volume of data that is readily accessible through automated workflow analysis. Features of interest will be determined either through user-defined parameters or through the AI identification of significant features in the collective data. This will allow meaningful statistics to be gathered about the size, shape, atomic structure, composition, electronic behaviour of potentially hundreds or thousands of regions in a given sample. This in turn will enable more complete understanding of nanostructure heterogeneity and structure-property relationships in materials.