<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/3A80ED30-8205-44AD-A40B-4B3B1400C68F" ns1:id="3A80ED30-8205-44AD-A40B-4B3B1400C68F"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/04F4A6C8-F6F3-4EA9-A35C-F97579B15ED1" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/A6A3C44D-DAA0-4967-8511-FF53B6B2D34D" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/A6A3C44D-DAA0-4967-8511-FF53B6B2D34D" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2026-04-29T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/D6E5A5A3-1C05-4C66-95DE-4B2CEC883215" ns1:rel="FUND" ns1:start="2025-11-01T00:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10172698</ns2:identifier></ns2:identifiers><ns2:title>Predicting Defects in Semiconductor Manufacturing with Artificial Intelligence</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Fast Start Response</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>Semiconductors are at the heart of modern life, powering smartphones, personal computers, data centers, medical devices, transportation, and advanced research. Fabricating semiconductor chips is one of the most complex forms of manufacturing, involving hundreds to over a thousand precise steps. At the nanometre scale (10,000th the width of the human hair), the smallest variations can cause defects that reduce manufacturing yield, increase costs, and waste energy and materials. For companies developing next-generation electronic, photonic, and quantum devices, these challenges slow innovation and delay the delivery of cutting-edge technologies to market.

This project will accelerate the development of artificial intelligence tools that can model critical steps in semiconductor manufacturing, helping companies make fabrication processes more efficient, reliable, and sustainable. The Growth Catalyst funding will allow DeepFab to expand its engineering and research team, providing the capacity needed to speed up AI tool development and achieve industrial-scale validation. The project will utilise the University of Southampton's leading nanofabrication facilities, which will provide a reliable environment and the high-quality data needed to refine the technology and prepare it for industry use.

The project will deliver a series of AI-based modelling tools that can predict critical stages in manufacturing semiconductors. In parallel, DeepFab will exhibit at a major international semiconductor manufacturing conference, showcasing its innovation, connecting with potential customers, and engaging global industry partners to guide the next stage of product development.

By reducing trial-and-error in manufacturing and supporting the UK's ambition to grow its semiconductor sector, the project will enhance national competitiveness, attract investment, and create high-value engineering jobs. It will also support more sustainable manufacturing practices, reducing environmental impact through lower energy use and material waste.

This project will help establish DeepFab as a leading UK innovator in AI-driven semiconductor manufacturing, providing benefits that reach beyond the company to the wider economy, the semiconductor supply chain, and the UK's global position in this internationally competitive industry.</ns2:abstractText></ns2:project>