With the advent of novel and more pervasive electronic applications, like point-of-care ones, developing an electronic paradigm that can avoid conventional signal reconstruction appears crucial. Neuromorphic computing gives the opportunity to directly process and store information without the need for complex acquisition, storage units, and other peripherals. In view of a more sustainable approach to disposable electronic applications, combining the concept of neuromorphic computing with transient, biocompatible, and even metabolizable technologies could be a viable path for next-generation green devices. In particular, edible devices could be a game-changing technology in food and health-related sectors, where information on the quality of food and a patient's health can be easily obtained by a digestible, safe, low-cost electronic platform.

This work presents a neuromorphic system for spatiotemporal pattern recognition with short-term synapses and neurons based on fully edible EGOFET made of copper phthalocyanine (CuPc). The devices are fabricated starting from ethyl cellulose substrate and interdigitated source and drain contacts of gold nanoparticles, where CuPc is evaporated as to serve as the edible semiconductor. Chitosan is drop-cast on top of the semiconductor and silver is printed to finalize the electrolyte-gate structure. The slow ion movement inside the chitosan, which is responsible for the double layer that enables the channel modulation of the CuPc, opens a hysteretic window visible in the transfer curve of the devices. Short-term plasticity with controllable and repeatable potentiation is enabled by carefully tuning the amplitude and pulse duration of the gate stimulation. The devices exhibit spontaneous depression with a relaxation time in the order of a second, compatible with the time constants of the mechanisms that take place inside the brain. We exploit this synaptic behavior to implement a reservoir computing architecture for number recognition, where the pixels are encoded in spatiotemporal pulses, with accuracy higher than 90%. We then combine the edible synapse with an electronic neuron that oscillates when a suitable potentiation level is reached at the synapse, thus acting as an activation function. The resulting neuromorphic circuit displays high operational and ambient stability, on par with more mature technologies. These results support CuPc-based edible EGOFETs as suitable elements to implement neuromorphic building blocks and pave the way for more sustainable and compact point-of-care devices.