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Start of funding 01.07.2020
Artificial Intelligence-Enabled Time-Stretch Terahertz-Spectroscopy for Physical and Biomedical Applications
Prof. Dr. Georg Herink
University of Bayreuth
Ultrafast Dynamics Lab
Prof. Dr. Bahram Jalali
University of California, Los Angeles (UCLA)
Department of Electrical and Computer Engineering
One of the central challenges in instrumentation is the fast measurement of material’s chemical and physical properties – and thus access to rapid irreversible phenomena. Terahertz (THz) spectroscopy is an emerging tool covering applications from solid-state physics, identification of chemical substances to biomedical detection in the far-infrared. While a host of techniques exist today, the established methods typically capture events that are repetitive and thus reproducible, and measure objects that stand still. However, recording of THz electric fields in single-shot is needed for high-throughput spectroscopy and for access to irreversible processes.
In this project, we develop an AI-enabled Terahertz sampling scheme that combines the “time-stretch” real-time data acquisition approach and recent advances in AI for data classification. Fundamentally, signal quality in real-time data acquisition is impaired by absence of high-resolution realtime digitizers. However, the required information encoded in raw signals is bound by several physical constraints, drastically reducing the final information content. As a proof-of-concept, we demonstrate this approach for data classification and extraction based on real-time THz-data.