Neurotransistor MatLab Code

Eunhye Baek, Nikhil Ranjan Das, Carlo Vittorio Cannistraci, Taiuk Rim, Gilbert Santiago Cañón Bermúdez, Khrystyna Nych, Hyeonsu Cho, Kihyun Kim, Chang-Ki Baek, Denys Makarov, Ronald Tetzlaff, Leon Chua, Larysa Baraban and Gianaurelio Cuniberti

Intrinsic plasticity of silicon nanowire neurotransistors for dynamic memory and learning functions

Nat Electron (2020). 

DOI: 10.1038/s41928-020-0412-1

Abstract: Neuromorphic architectures merge learning and memory functions within a single unit cell and in a neuron-like fashion. Research in the field has been mainly focused on the plasticity of artificial synapses. However, the intrinsic plasticity of the neuronal membrane is also important in the implementation of neuromorphic information processing. Here we report a neurotransistor made from a silicon nanowire transistor coated by an ion-doped sol–gel silicate film that can emulate the intrinsic plasticity of the neuronal membrane. The neurotransistors are manufactured using a conventional complementary metal–oxide–semiconductor process on an 8-inch (200 mm) silicon-on-insulator wafer. Mobile ions allow the film to act as a pseudo-gate that generates memory and allows the neurotransistor to display plasticity. We show that multiple pulsed input signals of the neurotransistor are non-linearly processed by sigmoidal transformation into the output current, which resembles the functioning of a neuronal membrane. The output response is governed by the input signal history, which is stored as ionic states within the silicate film, and thereby provides the neurotransistor with learning capabilities.

FIG: Illustration of the structural similarity between the ion migration in the neurotransistor (left) and the membrane of a neuron cell in which the ionic current was modulated by a membrane potential (Vmemb) change in the case of the action potential (right)

Code availability: The MatLab code that supports the mathematical model in this article is available
at https://github.com/eunhye8747/MatLab-Code-Neurotransistor

Acknowledgements: This research was supported by the German Excellence Initiative via the Cluster of Excellence EXC1056 Center for Advancing Electronics Dresden (CfAED) and the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ICT Consilience Creative Program (IITP-R0346-16-1007) supervised by the IITP (Institute for Information & communications Technology Promotion). We acknowledge support from the Initiative and Networking Fund of the Helmholtz Association of German Research Centers through the International Helmholtz Research School for Nanoelectronic Networks (IHRS NANONET) (no. VH‐KO‐606) and German Research Foundation (DFG) via grants MA 5144/9-1, MA 5144/13-1 and MA 5144/14-1; BA4986/7−1, BA4986/8−1. Finally, we thank the INSA-DFG Bilateral Exchange Programme for financial support (IA/ DFG/2018/138, 12 April 2018). The authors thank S. Oswald (IFW Dresden) for the X-ray photoemission spectroscopy analysis of the ion-doped hybrid silicate films and M. Park (NamLab, Dresden) for the insightful discussion about the ionic polarization in the film. We thank R. Nigmetzianov (TU Dresden) for the film analysis.