Intelligent Radiation Sensor Readout System​

Información del proyecto

i-RASE

Identificador del acuerdo de subvención: 101130550

DOI

10.3030/101130550

Fecha de la firma de la CE 6 Noviembre 2023

Fecha de inicio 1 Marzo 2024

Fecha de finalización 29 Febrero 2028

Financiado con arreglo a

• The European Innovation Council (EIC)

Coste total

€ 3 342 833,75

Aportación de la UE

€ 3 342 833,75

Coordinado por

DANMARKS TEKNISKE UNIVERSITET
Denmark

Periodic Reporting for period 1 - i-RASE (Intelligent Radiation Sensor Readout System​)

Período documentado: 2024-03-01 hasta 2025-02-28

Resumen del contexto y de los objetivos generales del proyecto

The Intelligent Radiation Sensor System (i-RASE) project is a groundbreaking initiative dedicated to revolutionizing radiation detection and imaging technology. The primary goal of the project is to develop a scalable and sustainable sensor system-in-package (SIP) that can perform real-time (RT) radiation detection and imaging with unprecedented accuracy and speed. Current radiation sensor technologies are often hampered by slow data processing, low measurement accuracy, and high digital data output. By integrating advanced artificial neural network (ANN) technology, i-RASE aims to overcome these limitations and provide a versatile solution applicable across healthcare, environmental monitoring, space exploration, security, and defense.

Trabajo realizado desde el comienzo del proyecto hasta el final del período abarcado por el informe y los principales resultados hasta la fecha

The i-RASE project began with a kick-off meeting on 06 March 2024 at DTU Space. Top-level and work package (WP) requirements were presented. After six months, a technical and management meeting at POLIMI reviewed milestones and ensured alignment with objectives. Despite minor delays, the project is on track.

WP1: Developed and tested theoretical detector models and ANN for real-time event classification. Established a feedback loop between DTU Space and DTU Compute.

WP2: Defined requirements for the sensor signal converter and test ASIC. Prepared for system-in-package integration.

WP3: Compiled digital interface requirements and reviewed test setups. Integrated ANN with realistic inputs.

WP4: KROMEK proposed a budget change. Delivered the D-matrix system with pixel detectors. Advanced requirements for next-gen sensors.

WP5: Set up ANN test environments and conducted initial tests with real data. Validated ANN's ability to process detector signals.

Next Steps: Refining ANN architectures, initiating ASIC fabrication, finalizing the digital platform, integrating advanced sensors, and maintaining robust project management.

i-RASE aims to deliver a novel radiation sensor system using real-time AI for enhanced detection and imaging.

Avances que van más allá del estado de la técnica e impacto potencial esperado (incluida la repercusión socioeconómica y las implicaciones sociales más amplias del proyecto hasta la fecha)

Real-Time ANN Integration
Embedded artificial neural networks for on-the-fly detector signal processing and event classification reduce data bottlenecks and surpass conventional offline analysis (cutting conventional event data size from ~50 KB/event to ~16 bytes/event for the 3D CZT DSD test detector). The system allows a direct connection to sensor signals for near-real-time, comprehensive signal processing, enabling the extraction of incident radiation information much faster and accurate.

Unified System-in-Package
Analog signal conversion, digital electronics, and AI are integrated into a single, compact, low-power system-in-package (SIP).

Physics-Informed Models
Synthetic data generation paired with real measurements accelerates robust ANN training and detector optimization. A potential IPR for a new ML method—combining physics-based parameters with real detector signals in an ANN-driven simulator—is under discussion.

Potential Impacts

Healthcare and Security: Faster, more accurate imaging and threat detection systems.
Environmental and Space: High-precision radiation monitoring with minimal hardware footprint, featuring intelligent on-board event processing software for background suppression and an improved signal-to-noise ratio based on radiation and interaction-type classification.
Key Needs for Further Uptake

Additional R&D and Demonstrations:
Explore cost-efficient detector materials to apply the i-RASE SIP approach for better detector performance and easier availability in cost-effective radiation detection and measurement applications.
Conduct ESRF test campaigns to confirm performance, reliability, and cost-effectiveness.
Access to Markets and Finance: Industry partnerships, private/public funding, and international collaborations.
Initial Proof of Concept Validated
Real-world 3D CZT DSD detector data confirm the feasibility of a simple trained ANN with enhanced speed and accuracy.

Prototype SIP
Early converter ASIC designs and digital platforms show promise for integrated, scalable radiation detection solutions.