Neural ordinary differential equations for predicting the temporal dynamics of a ZnO solid electrolyte FET

• Published in: Journal of materials chemistry. C, Materials for optical and electronic devices Vol. 13; no. 6; pp. 284 - 2813
• Main Authors: Gaurav, Ankit, Song, Xiaoyao, Manhas, Sanjeev Kumar, De Souza, Maria Merlyne
• Format: Journal Article
• Language: English
• Published: Cambridge Royal Society of Chemistry 06.02.2025
• Subjects: Co-design; Complex systems; Continuous time systems; Data processing; Differential equations; Electrolytes; Errors; Machine learning; Material properties; Ordinary differential equations; Pulse duration; Real time; Software; Solid electrolytes; Steady state models; Zinc oxide
• ISSN: 2050-7526; 2050-7534
• DOI: 10.1039/d4tc03696d

Efficient storage and processing are essential for temporal data processing applications to make informed decisions, especially when handling large volumes of real-time data. Physical reservoir computing provides effective solutions to this problem, making them ideal for edge systems. These devices typically necessitate compact models for device-circuit co-design. Alternatively, machine learning (ML) can quickly predict the behaviour of novel materials/devices without explicitly defining any material properties and device physics. However, previously reported ML device models are limited by their fixed hidden layer depth, which restricts their adaptability to predict varying temporal dynamics of a complex system. Here, we propose a novel approach that utilizes a continuous-time model based on neural ordinary differential equations to predict the temporal dynamic behaviour of a charge-based device, a solid electrolyte FET, whose gate current characteristics show a unique negative differential resistance that leads to steep switching beyond the Boltzmann limit. Our model, trained on a minimal experimental dataset successfully captures device transient and steady state behaviour for previously unseen examples of excitatory postsynaptic current when subject to an input of variable pulse width lasting 20-240 milliseconds with a high accuracy of 0.06 (root mean squared error). Additionally, our model predicts device dynamics in ∼5 seconds, with 60% reduced error over a conventional physics-based model, which takes nearly an hour on an equivalent computer. Moreover, the model can predict the variability of device characteristics from device to device by a simple change in frequency of applied signal, making it a useful tool in the design of neuromorphic systems such as reservoir computing. Using the model, we demonstrate a reservoir computing system which achieves the lowest error rate of 0.2% in the task of classification of spoken digits. A neural ordinary differential equation continuous time model of a ZnO/Ta 2 O 5 synaptic Solid Electrolyte FET, with a unique negative differential resistance in its gate current characteristics.