The cone photoreceptors in our eyes selectively transduce the natural light into spiking representations, which endows the brain with high energy-efficiency color vision. However, the cone-like device with color-selectivity and spike-encoding capability remains challenging. Here, we propose a metal oxide-based vertically integrated spiking cone photoreceptor array, which can directly transduce persistent lights into spike trains at a certain rate according to the input wavelengths. Such spiking cone photoreceptors have an ultralow power consumption of less than 400 picowatts per spike in visible light, which is very close to biological cones. In this work, lights with three wavelengths were exploited as pseudo-three-primary colors to form ‘colorful’ images for recognition tasks, and the device with the ability to discriminate mixed colors shows better accuracy. Our results would enable hardware spiking neural networks with biologically plausible visual perception and provide great potential for the development of dynamic vision sensors.

Synaptic plasticity divided into long-term and short-term categories is regarded as the origin of memory and learning, which also inspires the construction of neuromorphic systems. However, it is difficult to mimic the two behaviors monolithically, which is due to the lack of time-tailoring approaches for a certain synaptic device. In this Letter, indium-gallium-zinc-oxide (IGZO) nanofiber-based photoelectric transistors are proposed for realizing tunable photoelectric synaptic plasticity by the indium composition ratio. Notably, short-term plasticity to long-term plasticity transition can be realized by increasing the ratio of indium in the IGZO channel layer. The spatiotemporal dynamic logic and low energy consumption (<100 fJ/spike) are obtained in devices with low indium ratio. Moreover, the symmetric spike-timing-dependent plasticity is achieved by exploiting customized light and electric pulse schemes. Photoelectric long-term plasticity, multi-level characteristics, and high recognition accuracy (93.5%) are emulated in devices with high indium ratio. Our results indicate that such a composition ratio modulated method could enrich the applications of IGZO nanofiber neuromorphic transistors toward the photoelectric neuromorphic systems.

The cone photoreceptors in our eyes selectively transduce the natural light into spiking representations, which endows the brain with high energy-efficiency color vision. However, the cone-like device with color-selectivity and spike-encoding capability remains challenging. Here, we propose a vertical integrated spiking cone photoreceptor (VISCP) array with the structure of ITO/Ta2O5/Ag/IGZO/ITO, which can directly transduce persistent lights into spike trains at a certain rate according to the input wavelengths. The VISCPs have an ultralow power consumption of ≤400 pW per spike in visible light, which is very close to biological cones. In this work, lights with three wavelengths were exploited as pseudo three primary colors to form ‘colorful’ images for recognition tasks, and the device with the ability to discriminate mixed colors shows a better accuracy. Our results would enable hardware spiking neural networks with biologically plausible visual perception and provide great potential for the development of dynamic vision sensors.

True random number generators (TRNGs) can generate unpredictable binary bitstream by exploiting the intrinsic stochasticity in physical variables. In a threshold switching memristor, the stochastic forming/rupture of conducting pathway has been proved to be a good random source, while further improvement of high randomness and throughput is still a challenge. Here, a crossbar array of amorphous indium–gallium–zinc–oxide (a-IGZO)-based threshold switching memristors was designed for high-throughput TRNGs. The intrinsic stochasticity of Ag conductive filament in IGZO memristor and the stochastic sneak paths in the crossbar array are the two sources of randomness in our TRNGs. In our design, one input pulse train can produce multi-channel random bits, which enables a high scalability for such TRNGs. In addition, the average energy consumption of the TRNGs can be further reduced by increasing the integration scale of the memristors. Such IGZO-based TRNGs are of great significance for security applications.