@inproceedings{WellerHefenbrockTahoorietal.2020,
  author    = {Dennis D. Weller and Michael Hefenbrock and Mehdi Baradaran Tahoori and Jasmin Aghassi-Hagmann and Michael Beigl},
  title     = {Programmable Neuromorphic Circuit based on Printed Electrolyte-Gated Transistors},
  series = {ASP-DAC 2020. 25th Asia and South Pacific Design Automation Conference : Proceedings},
  publisher = {IEEE},
  isbn      = {978-1-7281-4123-7 (eBook)},
  issn      = {2153-697X (Online)},
  doi       = {10.1109/ASP-DAC47756.2020.9045211},
  pages     = {446 -- 451},
  year      = {2020},
  abstract  = {Neuromorphic computing systems have demonstrated many advantages for popular classification problems with significantly less computational resources. We present in this paper the design, fabrication and training of a programmable neuromorphic circuit, which is based on printed electrolytegated field-effect transistor (EGFET). Based on printable neuron architecture involving several resistors and one transistor, the proposed circuit can realize multiply-add and activation functions. The functionality of the circuit, i.e. the weights of the neural network, can be set during a post-fabrication step in form of printing resistors to the crossbar. Besides the fabrication of a programmable neuron, we also provide a learning algorithm, tailored to the requirements of the technology and the proposed programmable neuron design, which is verified through simulations. The proposed neuromorphic circuit operates at 5V and occupies 385mm 2 of area.},
  language  = {en}
}