A scalable solution recipe for a Ag-based neuromorphic device.

Integration and scalability have posed significant problems in the advancement of brain-inspired intelligent systems. Here, we report a self-formed Ag device fabricated through a chemical dewetting process using an Ag organic precursor, which offers easy processing, scalability, and flexibility to address the above issues to a certain extent. The conditions of spin coating, precursor dilution, and use of solvents were varied to obtain different dewetted structures (broadly classified as bimodal and nearly unimodal). A microscopic study is performed to obtain insight into the dewetting mechanism. The electrical behavior of selected bimodal and nearly unimodal devices is related to the statistical analysis of their microscopic structures. A capacitance model is proposed to relate the threshold voltage (Vth) obtained electrically to the various microscopic parameters. Synaptic functionalities such as short-term potentiation (STP) and long-term potentiation (LTP) were emulated in a representative nearly unimodal and bimodal device, with the bimodal device showing a better performance. One of the cognitive behaviors, associative learning, was emulated in a bimodal device. Scalability is demonstrated by fabricating more than 1000 devices, with 96% exhibiting switching behavior. A flexible device is also fabricated, demonstrating synaptic functionalities (STP and LTP).

Emulating learning behavior in a flexible device with self-formed Ag dewetted nanostructure as active element.

Neuromorphic devices are a promising alternative to the traditional von Neumann architecture. These devices have the potential to achieve high-speed, efficient, and low-power artificial intelligence. Flexibility is required in these devices so that they can bend and flex without causing damage to the underlying electronics. This feature shows a possible use in applications that require flexible electronics, such as robotics and wearable electronics. Here, we report a flexible self-formed Ag-based neuromorphic device that emulates various brain-inspired synaptic activities, such as short-term plasticity and long-term potentiation (STP and LTP) in both the flat and bent states. Half and full-integer quantum conductance jumps were also observed in the flat and bent states. The device showed excellent switching and endurance behaviors. The classical conditioning could be emulated even in the bent state.

Emulating Ebbinghaus forgetting behavior in a neuromorphic device based on 1D supramolecular nanofibres.

Mimicking synaptic functions in hardware devices is a crucial step in realizing brain-like computing beyond the von Neumann architecture. 1D nanomaterials with spatial extensions of a few µm, similar to biological neurons, gain significance given the ease of electrical transport as well as directionality. Herein, we report a two-terminal optically active device based on 1D supramolecular nanofibres consisting of CS (coronene tetracarboxylate) and DMV (dimethyl viologen) forming alternating D-A (donor-acceptor) pairs, emulating synaptic functions such as the STP (short-term potentiation), LTP (long-term potentiation), PPF (paired-pulse facilitation), STDP (spike-time dependent plasticity) and learning-relearning behaviors. In addition, an extensive study on the less explored Ebbinghaus forgetting curve has been carried out. The supramolecular nanofibres being light sensitive, the potential of the device as a visual system is demonstrated using a 3 × 3 pixel array.