MALMO – Tecniospring INDUSTRY

Poster European Corner

Nom i cognoms / Name and surname

Ignacio Becerril Romero

Afiliació / Affiliation

Institut de Recerca en Energia de Catalunya (IREC)

Programa de finançament europeu en que s’enmarca aquest projecte? / European funding programme in which this project is being carried out?

Marie Skłodowska-Curie Schemes

Títol del projecte / Project title

New machine Learning process monitoring methodologies based on spectroscopic techniques for in-line inspection of thin film PV industrial manufacturing processes (MALMO) (Tecniospring INDUSTRY)

Número del projecte / Project number


Breu explicació del projecte / Brief explanation of your project

Thin film photovoltaics (TFPV) devices represent a step forward for solar energy as they expand the niches of application of PV and can be mass-produced in a cost-effective way. However, their industrial production is highly complex and small manufacturing deviations result in defective products and waste of high value materials, energy and time. In this context, MALMO is devoted to developing and demonstrating advanced statistical and machine learning process monitoring methodologies for the TFPV industry that minimize this issue enabling an early detection of deviations during manufacturing. These are implemented in a XRF-Raman-photoluminescence (PL) spectroscopic inspection platform in an existing industrial Cu(In,Ga)Se2 (CIGS) solar foil roll-to-roll (R2R) manufacturing pilot line (SUNPLUGGED GmbH, Austria).

 Enllaç a la pàgina web del projecte / Link to your project website

Repte en que s’emmarca aquest projecte / Challenge within the framework of this project

1. Adaptation to Climate Change: support at least 150 European regions and communities to become climate resilient by 2030, 4. 100 Climate-Neutral and Smart Cities by 2030



Amb el suport de:

Amb el finançament de:

Aquest projecte està cofinançat pel programa de recerca i innovació Horizon Europe de la Unió Europea sota el projecte NitRecerCat (101061189).

This project is co-funded by the European Union’s research and innovation programme Horizon Europe, under the project NitRecercat (101061189).