Realizing linear synaptic plasticity in electric double layer-gated transistors for improved predictive accuracy and efficiency in neuromorphic computing

Abstract Neuromorphic computing offers a low-power, parallel alternative to traditional von Neumann architectures by addressing the sequential data processing bottlenecks. Electric double layer-gated transistors (EDLTs) resemble biological synapses with their ionic response and offer low power operations, making them suitable for neuromorphic applications. A critical consideration for artificial neural networks (ANNs) is achieving linear and symmetric plasticity (i.e. weight updates) during training, as this directly affects accuracy and efficiency. This study uses finite element modeling to explore EDLTs as artificial synapses in ANNs and investigates the underlying mechanisms behind the nonlinear weight updates observed experimentally in previous studies. By solving modified Poisson–Nernst–Planck equations, we examined ion dynamics within an EDL capacitor and their effects on plasticity, revealing that the rates of EDL formation and dissipation are concentration-dependent. Fixed-magnitude pulse inputs result in decreased formation and increased dissipation rates, leading to nonlinear weight updates. For a pulse magnitude of 1 V, both 1 ms 500 Hz and 5 ms 100 Hz pulse inputs saturated at less than half of the steady state EDL concentration, limiting the number of accessible states and operating range of devices. To address this, we developed a predictive linear ionic weight update solver (LIWUS) in Python to predict voltage pulse inputs that achieve linear plasticity. We then evaluated an ANN with linear and nonlinear weight updates on the Modified National Institute of Standards and Technology classification task. The ANN with LIWUS-provided linear weight updates required 19% fewer (i.e. 5) epochs in both training and validation than the network with nonlinear weight updates to reach optimal performance. It achieved a 97.6% recognition accuracy, 1.5–4.2% higher than with nonlinear updates, and a low standard deviation of 0.02%. The network model is amenable to future spiking neural network applications, and the performance with linear weight update s is expected to improve for complex networks with multiple hidden layers.