Zero-Energy Neuromorphic Visual Computing with Photocurrent-Based Memory in ZnMgO/Se Synapses

As artificial intelligence continues to evolve and become ubiquitous, the limitations of traditional Von Neumann computing architectures become increasingly obvious, both in term 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 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 ultralow power neuromorphic systems.
We demonstrated last year the capability of these ZnMgO/Se photodiodes to mimic advanced synaptic behaviours through light-induced plasticity leveraging the mechanism of trapping photocarrier 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.
To characterise the synaptic plasticity, we perform a detailed analysis of the post-synaptic plasticity in response to various pulsed light stimuli and we 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, mimicking biological synapses. This precise temporal control over the plasticity opens the door to complex learning algorithms within visual neuromorphic systems, without the need of a read voltage as all measurements are performed in short circuit conditions.
We demonstrated that the memory state of the synapse can be both written and read by monitoring changes in the short-circuit current (Jsc), eliminating the need for an external power source during operation. The non-volatile memory effect is reset can be accelerated through either reverse voltage bias spikes or via the mechanism of the piezophototronic effect, where mechanical stress changes the band alignment at the ZnMgO/Se interface and creates a piezopotential which empties the metastable traps, thus resetting the system without consuming additional energy.
Wavelength selectivity is also investigated over a wide array of illuminations ranging from UV (350nm) to IR (1450nm), indicating that the ZnMgO/Se interface can be tuned to simulate colour-sensitive synaptic responses, similar to photoreceptors in the retina.
Our study demonstrates the potential of inorganic thin-film ZnMgO/Se heterojunctions in visual neuromorphic computing. It positions these devices as a promising candidate for future hardware-based AI-driven applications, particularly in edge computing.