Tommaso Stecconi, Roberto Guido, et al.
Advanced Electronic Materials
Recent progress in the development of phase-change materials is enabling their use for novel approaches in spike-based learning circuits and brain-inspired computing architectures [1], [2]. The race to leverage efficient low-power neural processing systems whose fundamental operations are akin to those of animal brains drives the community to explore new 'neuromorphic' materials and devices [3]. Amongst the candidates suitable to deliver such types of devices, vanadium-dioxide (VO2) offers promising features [4]. Its polycrystalline morphology, once integrated on a Si platform, has the ability to oscillate from a high to a low resistive statenear room temperature (68°C) [5]. These natural oscillations triggered by biasing VO2 at low voltage provide means to build a network of electronic oscillators with tremendous potential for neural network architectures, AI applications, and optimization capabilities [1], [3], [6], [7]. In particular, oscillation-based computing serves best to solve constraint-satisfaction problems [8], [9]. Any optimization problem, simple in appearance, typically requires heavy computing resources calling for long processing times and even larger energy consumption [2]. In this work, we show the power and the number of cycles needed to reach a solution can be dramatically reduced to solve NP-hard problems with our befitted VO2-based 3\times 3 oscillating neural networks (ONNs).
Tommaso Stecconi, Roberto Guido, et al.
Advanced Electronic Materials
Pavlos Maniotis, Daniel M. Kuchta
J. of Opt. Comm. and Netw.
Max Bloomfield, Amogh Wasti, et al.
ITherm 2025
Victor Chan, A. Gasasira, et al.
IEEE Trans Semicond Manuf