Jakob Finkbeiner

M. Sc.

Research staff member
Institute of Electrical and Optical Communications
IC group

Contact

+49 711 685 67899
 +49 711 685 67900
Business card (VCF)

Pfaffenwaldring 47
70569 Stuttgart
Germany
Room: 2.408

Subject

My research topic deals with the interface between electrical and optical communications. Basically, the challenge here is to enable the fastest, most efficient and most accurate conversion of an optical signal into an electrical one and vice versa. Specifically, I am currently researching the use of artificial neural networks for optical data transmission. The aim is to efficiently and quickly compensate disturbances such as noise or nonlinear distortions from the transmission channel. The neural networks will be realized by energy efficient mixed-signal neurons, which potentially require a fraction of the energy of classical, digital neurons for computing operations.

  1. 2025

    1. J. Finkbeiner, R. Nägele, M. Wittlinger, M. Grözing, M. Berroth, and G. Rademacher, “Analoge Berechnung von Künstlichen Neuronalen Netzen in 22 nm FD-SOI CMOS,” in Analog Workshop, 2025, pp. 18–19.
    2. J. Finkbeiner, “An Energy-Efficient Voltage-to-Time Converter With Built-in ReLU Activation Function in 22-nm FDSOI: SSCS Circuit Analysis and Design Contest: The Winners of the 2024 Edition,” IEEE Solid-State Circuits Magazine, vol. 17, pp. 119–122, 2025.
    3. M. Grözing, R. Nägele, J. Finkbeiner, and G. Rademacher, “Two-path operational amplifier using FDSOI CMOS with first fast low-gain and second slow high-gain path for fast regulated voltage buffers with low offset error,” in Analog Workshop, 2025, pp. 26–29.
    4. M. Wittlinger, J. Finkbeiner, R. Nägele, M. Grözing, M. Berroth, and G. Rademacher, “Glitch-freier, jitterarmer Amplituden- und Phasenschalter für digitale RF-Pulsmodulation,” in Analog Workshop, 2025, pp. 43–44.

  2. 2024

    1. J. Finkbeiner, R. Nägele, M. Grözing, M. Berroth, and G. Rademacher, “Characterization of a Femtojoule Voltage-to-Time Converter with Rectified Linear Unit Characteristic for Analog Neural Network Inference Accelerators,” in IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2024, pp. 253–257.
    2. R. Nägele, J. Finkbeiner, M. Grözing, M. Berroth, and G. Rademacher, “Characterization of an Analog MAC Cell with Multi-Bit Resolution for AI Inference Accelerators,” in IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2024, pp. 243–247.
    3. J. Finkbeiner, R. Nägele, M. Grözing, M. Berroth, and G. Rademacher, “Ultra-energy-efficient analog multiply-accumulate and rectified linear unit circuit for artificial neural network inference accelerators,” in International Conference on Neuromorphic Computing and Engineering 2024 (ICNCE), 2024.

  3. 2023

    1. R. Nägele, J. Finkbeiner, V. Stadtlander, M. Grözing, and M. Berroth, “Analog Multiply-Accumulate Cell with Multi-Bit Resolution for All-Analog AI Inference Accelerators,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 2023, pp. 1–13, 2023.
    2. J. Finkbeiner, P. Thomas, C. Schweikert, M. Grözing, M. Berroth, and G. Rademacher, “Monolithically Integrated Optoelectronic Receiver Front-End,” in Optica Advanced Photonics Congress: Workshop on Ultrafast Signal Processing by Combined Photonic-Electronic Integrated Systems, Busan, Republic of South Korea, 2023.

  4. 2022

    1. P. Thomas, J. Finkbeiner, M. Grözing, and M. Berroth, “Time-Interleaved Switched Emitter Followers to Extend Front-End Sampling Rates up to 200 GS/s,” IEEE Journal of Solid State Circuits, pp. 1–12, 2022.
    2. J. Finkbeiner, N. Hoppe, P. Thomas, and C. Schweikert, “Extreme broadband and reconfigurable integrated photonic electronic receiver on silicon substrate,” in EUROPRACTICE activity report 2021 - 2022, 2022, p. 25.
    3. J. Finkbeiner, R. Nägele, M. Berroth, and M. Grözing, “Design of an Energy Efficient Voltage-to-Time Converter with Rectified Linear Unit Characteristics for Artificial Neural Networks,” in IEEE International New Circuits and Systems Conference (NEWCAS), 2022, pp. 327–331.
    4. R. Nägele, J. Finkbeiner, M. Berroth, and M. Grözing, “Design of an Energy Efficient Analog Two-Quadrant Multiplier Cell Operating in Weak Inversion,” in IEEE International New Circuits and Systems Conference (NEWCAS), 2022, pp. 5–9.

  5. 2021

    1. J. Finkbeiner, “Integrierter Entwurf einer Optoelektronischen Eingangsschaltung für einen Empfänger in Glasfasernetzwerken.” 2021.

  6. 2018

    1. L. Rathgeber, N. Hoppe, J. Finkbeiner, T. Föhn, W. Vogel, and M. Berroth, “Tuneable Optical Devices Using Subwavelength Structures,” in Joint Symposium on Opto- and Microelectronic Devices and Circuits (SODC), Aachen, Germany, 2018.
    2. J. Finkbeiner, “Simulation von Sub-Wellenlängen-Wellenleitern.” 2018.
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