Dresden 2026 – scientific programme

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HL: Fachverband Halbleiterphysik

HL 2: Focus Session: Biocompatible Organic Semiconductors for Artificial Intelligence

HL 2.6: Talk

Monday, March 9, 2026, 12:15–12:30, POT/0051

Reservoir computing with mixed ionic-electronic conductors — •Richard Kantelberg, Hans Kleemann, and Karl Leo — Institut für Angewandte Physik, TU Dresden

Reservoir computing (RC) is a promising paradigm for machine learning that utilizes dynamic systems, termed as reservoirs, to process and analyze complex temporal data. Organic mixed ionic electronic conductors (OMIECs) have emerged as a novel class of materials with intriguing properties, such as their ability to exhibit both electronic and ionic conductivity, as well as their biocompatibility, flexibility, and low power consumption[1]. These features make OMIECs particularly suitable for the development of unconventional computing architectures in the field of bioelectronics[2]. We present recent findings interlinking electronic conductivity, system nonlinearity and reservoir size with the neuromorphic functionality and RC performance of self-organized and structured OMIEC reservoirs. This study includes novel insights into the differences and similarities of p-type, n-type and ambipolar semiconductors in terms of operation speed and energy consumption. The recent progress in reservoir computing using organic mixed ionic electronic provides valuable knowledge for the targeted development OMIEC reservoirs.

References 1.*Paulsen, B. D., Tybrandt, K., Stavrinidou, E. & Rivnay, J. Organic mixed ionic*electronic conductors. Nat. Mater. 19, 13*26 (2020). 2.*van de Burgt, Y., Melianas, A., Keene, S. T., Malliaras, G. & Salleo, A. Organic electronics for neuromorphic computing. Nat. Electron. 1, 386*397 (2018).

Keywords: neuromorphic; reservoir computing; echo state network; organic electronics; analog computing