As Moore’s law slows down and memory-
intensive tasks get prevalent, digital computing becomes increasingly capacity- and power-limited. In order to meet the requirement for increased computing capacity and efficiency in the post-Moore era, emerging computing architectures, such as in-memory computing and neuromorphic computing architectures based on memristors, have been extensively pursued and become an important candidate for new-generation non-von Neumann computers. Since the connection of the theoretical memristor concept with！Resistive switching devices in 2008, tremendous progress has been made in their applications in-memory and computing systems.
Here, we report an optoelectronic synapse that has controllable temporal dynamics under electrical and optical stimuli. Tight coupling between ferroelectric and optoelectronic processes in the synapse can be used to realize heterosynaptic plasticity, with relaxation timescales that are tunable via light intensity or back-gate voltage. We use the synapses to create a multimode reservoir computing system with adjustable nonlinear transformation and multisensory fusion, which is demonstrated using a multimode handwritten digit recognition task and a QR code recognition task. We also realize a multiscale reservoir computing system via the tunable relaxation timescale, which is tested using a temporal signal prediction task.