Process Integration

El-Kareh, Badih; Hutter, Lou N.

doi:10.1007/978-1-4939-2751-7_9

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…Notably, W/WO 2 and WO 3-x /Au exhibit nonrectifying behavior, signifying Ohmic contacts. [68] This observation is consistent with the metallic characteristics of WO 2 , which results in a linearly adjustable resistance upon contact with W. [69] Furthermore, as N-type semiconductors, both WO 2 and WO 3-x have work functions higher than metals(W/Au), indicating that the surface electron concentration of semiconductors is higher than that inside the material, resulting in the formation of a reverse barrier layer. Therefore, under forward bias, highconcentration electrons easily flow towards the metal, and under reverse bias, the applied electric field is opposite to the built-in electric field, allowing electrons in the metal to readily cross the barrier and enter the semiconductor, thus forming Ohmic contact.…”

Section: Resultssupporting

confidence: 64%

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

Cai,

Liu,

Wang

et al. 2024

Adv Funct Materials

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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.

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Section: Resultssupporting

confidence: 64%