iDev40 · Project

Digital Twins and AI to Cut Time-to-Market for Electronics Manufacturing

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Imagine you're building a complex electronic product — chips, sensors, car electronics. Right now, designing it, testing it, and getting it to the factory floor takes forever because every team uses different tools that don't talk to each other. iDev40 built a way to create a complete digital copy of your product and production line — a "digital twin" — so engineers can simulate, test, and fix problems on screen before touching real hardware. Think of it like architects using 3D models before pouring concrete, but for entire electronics supply chains with AI helping spot issues early.

By the numbers

consortium partners validating the technology

countries across which the approach was tested

industry partners involved in development and testing

SMEs participating in the consortium

51%

industry ratio in the consortium

project deliverables produced

The business problem

What needed solving

Electronics manufacturers face crushing pressure to get products to market faster, but development, simulation, and production run on disconnected systems. Engineers waste time re-entering data, re-running tests, and fixing problems that could have been caught earlier with better digital tools. The result: delayed launches, higher costs, and lost competitive advantage — especially against non-European competitors who move faster.

The solution

What was built

The project delivered 24 outputs including a working decision support system demonstrator for complexity management on the shop floor and at management level. The core technology integrates AI learning, digital twins, data life cycle management, and supply chain knowledge loops into a connected development-to-production digital thread.

Audience

Who needs this

Semiconductor fabs and chip design houses looking to cut development cyclesAutomotive Tier 1 suppliers managing increasingly complex electronic systemsIndustrial IoT platform companies building smart factory solutionsElectronics contract manufacturers handling high-mix productionR&D-intensive manufacturers struggling with design-to-production handoff

Business applications

Who can put this to work

Semiconductor manufacturing

enterprise

Target: Chip manufacturers and electronics component makers

If you are a semiconductor company dealing with long development cycles and costly physical prototyping — this project developed digital twin technology and AI-driven decision support systems that link design, simulation, and production into one digital thread. The demonstrator was tested for complexity management on the shop floor and at management level. With 41 partners across 6 countries validating the approach, this targets real production environments.

Automotive electronics

enterprise

Target: Tier 1 and Tier 2 automotive suppliers producing electronic control units

If you are an automotive electronics supplier struggling to keep up with accelerating model cycles and increasing chip complexity — this project built integrated development tools that close the knowledge loop across the entire product life cycle and supply chain. The consortium included 21 industry partners who proved the technologies on selected use cases in real productive environments, meaning the tools were designed for actual factory conditions, not just labs.

Industrial automation

mid-size

Target: Companies building smart factory and IoT solutions for manufacturing

If you are an industrial automation provider whose customers demand faster deployment of connected production systems — this project created methods for embedding digital development directly into digital production. The decision support system demonstrator handles complex demands in work systems at both shop floor and management level, giving your team real-time planning support instead of guesswork.

Frequently asked

Quick answers

What would it cost to implement these digital twin tools in our factory?

The project data does not include specific licensing or implementation costs. Since the consortium was led by Infineon Technologies Austria and included 21 industry partners, commercial terms would need to be negotiated directly with the technology providers who developed and tested specific components.

Can this scale to our production volume and complexity?

The project specifically targeted complexity management in real productive environments. The decision support system demonstrator was designed for both shop floor operations and management-level planning, suggesting it handles multi-level industrial complexity. With 41 partners across 6 countries testing the approach, scalability across different production setups was a core design goal.

Who owns the IP and can we license specific tools?

IP is distributed among the 41 consortium partners, with Infineon Technologies Austria AG as coordinator. The project explicitly addressed IP Protection as one of its core themes. Licensing arrangements would need to be discussed with the specific partner that developed the component you need.

How does this integrate with our existing manufacturing execution systems?

The project was built around the principle of seamlessly integrating development with automation and network solutions already in place. It specifically aimed to combine existing diagnostics algorithms, machine controls, simulators, and optimizers into an effective integration layer. Based on available project data, the approach was designed to work with state-of-the-art industrial tools rather than replace them.

What is the realistic timeline from evaluation to production use?

The project ran from 2018 to 2021 and produced 24 deliverables including a working demonstrator for decision support. As an Innovation Action with technologies proven in real productive environments, the tools are past the research stage. Implementation timeline would depend on your specific production setup and which components you adopt.

Is this compliant with European data and manufacturing regulations?

The project was funded under ECSEL (Electronic Components and Systems for European Leadership) and specifically addressed Data Life Cycle Management. Based on available project data, the consortium included partners from 6 EU countries, and EU-funded projects of this type must comply with European data governance standards.

Consortium

Who built it

This is a heavyweight industrial consortium led by Infineon Technologies Austria, one of Europe's largest semiconductor manufacturers. With 41 partners across 6 countries (Austria, Belgium, Germany, Spain, Italy, Romania), the project has strong geographic and sectoral coverage. The 51% industry ratio — 21 industry partners including 8 SMEs — means the technology was developed with real manufacturing needs driving the requirements, not just academic curiosity. The remaining 13 universities and 7 research organizations provided the scientific backbone. For a business buyer, this consortium composition signals that the outputs were built for production environments, not just published in journals.

INFINEON TECHNOLOGIES AUSTRIA AGCoordinator · AT
INSTITUTO IBERMATICA DE INNOVACION SLthirdparty · ES
Infineon Technologies Romania and CO. Societate in Comandita Simplaparticipant · RO
UNIVERSITAET SIEGENparticipant · DE
TECHNISCHE UNIVERSITAET WIENparticipant · AT
SIRRIS HET COLLECTIEF CENTRUM VAN DE TECHNOLOGISCHE INDUSTRIEparticipant · BE
AIT AUSTRIAN INSTITUTE OF TECHNOLOGY GMBHparticipant · AT
CISC SEMICONDUCTOR GMBHparticipant · AT
FUNDACION TECNALIA RESEARCH & INNOVATIONparticipant · ES
FERNUNIVERSITAT IN HAGENparticipant · DE
ELMOS SEMICONDUCTOR SEparticipant · DE
SYSTEMA SYSTEMENTWICKLUNG DIPL INF.MANFRED AUSTEN GMBHparticipant · DE
AYESA IBERMATICA SAparticipant · ES
INFINEON TECHNOLOGIES AGparticipant · DE
INFINEON TECHNOLOGIES ITALIA SRLparticipant · IT
OSTBAYERISCHE TECHNISCHE HOCHSCHULE REGENSBURGparticipant · DE
UNIVERSITATEA TEHNICA CLUJ-NAPOCAparticipant · RO
EVOLARIS NEXT LEVEL GMBHparticipant · AT
ECCENCA GMBHparticipant · DE
VIRTUAL VEHICLE RESEARCH GMBHparticipant · AT
INFINEON TECHNOLOGIES IT-SERVICES GMBHparticipant · AT
TTTECH INDUSTRIAL AUTOMATION AGparticipant · AT
KAI KOMPETENZZENTRUM AUTOMOBIL - UND INDUSTRIEELEKTRONIK GMBHparticipant · AT
GIESECKE+DEVRIENT EPAYMENTS GMBHparticipant · DE
AVL LIST GMBHparticipant · AT
Hochschule fuer Technik und Wirtschaft Dresdenparticipant · DE
UNIVERSITATEA POLITEHNICA DIN BUCURESTIparticipant · RO
TTTECH COMPUTERTECHNIK AGparticipant · AT
UNIVERSITAET KLAGENFURTparticipant · AT
UNIVERSITA' DEGLI STUDI DI MILANO-BICOCCAparticipant · IT
WESTSACHSISCHE HOCHSCHULE ZWICKAUparticipant · DE
WIRTSCHAFTSUNIVERSITAT WIENparticipant · AT
HOCHSCHULE ZITTAU/GOERLITZparticipant · DE
INFINEON TECHNOLOGIES DRESDEN AG & Co. KGparticipant · DE
KNOW CENTER RESEARCH GMBHparticipant · AT
JEMA ENERGY SAparticipant · ES
TECHNISCHE UNIVERSITAET DRESDENparticipant · DE

How to reach the team

Infineon Technologies Austria AG — reach out to their R&D partnerships or technology transfer office

Order a report on this consortium See INFINEON TECHNOLOGIES AUSTRIA AG profile →

Next steps

Talk to the team behind this work.

Want an introduction to the iDev40 team? SciTransfer can connect you with the right consortium partner for your specific manufacturing challenge.

Order a report on this topic More in Manufacturing & Industry 4.0