As artificial intelligence continues to evolve and become ubiquitous, the limitations of traditional Von Neumann computing architectures become increasingly obvious, both in terms of energy consumption and for tackling complex, probabilistic tasks typical in real-world data. Inspired by the human brain, neuromorphic computing integrates memory and computation directly through a network of artificial synapses. As more than 80% of the brain’s input is visual, the development of artificial visual synapses is a very promising solution, particularly for edge computing, where data is processed locally, reducing latency and bandwidth usage. In this work, we explore the integration of ZnMgO/Se heterojunction photodiodes as artificial visual synapses, combining both photodetection and memory functions to enable ultra-low power neuromorphic systems.

We demonstrated last year the capability of these ZnMgO/Se photodiodes to mimic advanced synaptic behaviors through light-induced plasticity, leveraging the mechanism of trapping photocarriers in metastable interface defects at the ZnMgO/Se junction to simulate plasticity through persistent photoconductivity. However, our previous work required an external energy input to read the memory state of the system (read voltage). In this work, we demonstrate how these defects can modulate the system’s photocurrent in response to light stimulation in real time. Upon illumination, a non-volatile increase or decrease in the device’s photocurrent is observed, depending on the light intensity, duration, and photon energy. This phenomenon, referred to as the light soaking effect in thin-film solar cells, allows the photodiode to emulate fundamental synaptic functionalities with zero external power input beyond the visual stimulation, including short-term potentiation (STP), long-term potentiation (LTP), and their corresponding depression mechanisms. As the memory state of the synapse can be both written and read by monitoring changes in the short-circuit current (Jsc), it eliminsates the need for an external power source during operation.To characterize the synaptic plasticity, we perform a detailed analysis of the post-synaptic plasticity in response to various pulsed light stimuli and demonstrate short-term potentiation through paired-pulse facilitation (PPF). Our results show a significant increase in the PPF ratio as a function of the inter-stimulus interval reduction. Moreover, we investigated spike rate-dependent plasticity (SRDP), where the device exhibited enhanced photocurrent responses at higher stimulation frequencies, as well as spike timing-dependent plasticity (STDP), mimicking biological synapses. This precise temporal control over plasticity opens the door to complex learning algorithms within visual neuromorphic systems, without the need for a read voltage, as all measurements are performed in short-circuit conditions. Remarkably, we demonstrate the possiblity of analogically switching between potentiation and depression by stimulation with different wavelengths. This significantly increases the possibilities in terms of programming states, and provides important insights is terms of physical interpretation of the mechanism responsible for the charge trapping-detrapping at the p-n interface.

Our study demonstrates the potential of inorganic thin-film ZnMgO/Se heterojunctions in visual neuromorphic computing, positioning these devices as a promising candidate for future hardware-based AI-driven applications.