Energy-efficient memcapacitor devices for neuromorphic computing

Data-intensive computing operations, such as training neural networks, are essential for applications in artificial intelligence but are energy intensive. One solution is to develop specialized hardware onto which neural networks can be directly mapped, and arrays of memristive devices can, for example, be trained to enable parallel multiply-accumulate operations. Here we show that memcapacitive devices that exploit the principle of charge shielding can offer a highly energy-efficient approach for implementing parallel multiply-accumulate operations. We fabricate a crossbar array of 156 microscale memcapacitor devices and use it to train a neural network that could distinguish the letters 'M', 'P' and 'I'. Modelling these arrays suggests that this approach could offer an energy efficiency of 29,600 tera-operations per second per watt, while ensuring high precision (6-8 bits). Simulations also show that the devices could potentially be scaled down to a lateral size of around 45 nm.

Kai-Uwe Demasius, Aron Kirschen, Stuart Parkin