Altering Kinetics of Memimpedor Devices for Neuromorphic Computing
Dip Das a, Dovydas Joksas a, Markus Hellenbrand b, Judith MacManus-Driscoll b, Tony Kenyon a, Adnan Mehonic a
a Electronic & Electrical Engineering - University College London
b Materials Science and Metallurgy Department, University of Cambridge - UK, Quayside, 1, United Kingdom
Proceedings of Neuronics Conference (Neuronics)
València, Spain, 2024 February 21st - 23rd
Organizers: Sabina Spiga and Juan Bisquert
Oral, Dip Das, presentation 007
Publication date: 18th December 2023

Novel nanoelectronics technologies have been utilized to develop systems capable of running various artificial neural network (ANN) architectures, presenting the potential to power future edge devices. Memristor-based ANN architectures may be particularly appealing due to their in-memory processing capabilities, high throughput, and energy efficiency. We hypothesize that systems capable of directly processing complex-valued signals (e.g., those originating from the external world), could be more efficient than systems that necessitate the separation and independent processing of amplitude and phase information, which is the case form most conventional systems. Here, we introduce innovative nanodevices utilizing W/Ba-doped HfO2/Nb:SrTiO3 stacks, which exhibit simultaneous conductance and capacitance changes during both set and reset processes, establishing them as both memristive and memcapacitive devices. These interface-type switching devices leverage multiple impedance states and allow us to gradually alter set and reset kinetics. This concept could provide a foundation for the development of systems that directly implement complex-valued neural networks (CVNNs). This allows us to reduce the circuit complexity, number of training epochs, and accordingly the overall energy efficiency to distinguish them from any traditional two-dimensional real-valued neural networks (RVNN). Overall, this study paves the way for the development of energy-efficient neuromorphic hardware.

A.M. and D.D. would like to acknowledge funding from the Engineering and Physical Sciences Research Council  (EP/X018431/1)

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