BCM-Inspired Synapses Constructed with Barrier-Modulated Coupling Junctions for Enhancing Speech Recognition

Bio-inspired synaptic devices have garnered considerable interest in neuromorphic computing. The Bienenstock-Cooper-Munro (BCM) learning rule stands out as one of the most accurate synaptic models, featuring non-monotonic behavior and threshold sliding effect, crucial for stable learning processes. The direct device strategy for completely mimicking the BCM rule is a tough issue since the current devices lack two competitive working modes within one device. In this work, a dual-junction synaptic device with opposite built-in electric fields using a W/WO2/WO3-x/Au structure is demonstrated. The devices directly mimic two fundamental features of the BCM rule via a delicately-designed bandgap engineering strategy. Furthermore, the working mechanisms are investigated and the promising potential of dual-junction synaptic devices is demonstrated for enhancing speech recognition through Convolutional Neural Network (CNN)-based digital speech recognition with a remarkable accuracy of 98% through a synaptic array. Even for speech recognition with 13% Gaussian noise, the accuracy remained at 83%. These findings provide a promising strategy for developing BCM-based synaptic devices for neuromorphic computing applications. A delicately-designed bandgap engineering strategy simulates the BCM Rule. The dual-junction synaptic device incorporates opposite built-in electric fields through a W/WO2/WO3-x/Au structure. The devices faithfully mimic two fundamental features of the BCM rule: non-monotonicity and threshold sliding effect. These behaviors are applied to enhance the characteristics of audio signals, which is essential for improving the accuracy of CNN-based speech recognition. image