Highly-dependable printed neuromorphic circuits based on additive manufacturing

The rapid development of emerging domains, such as the Internet of Things and wearable technologies, necessitates the development of flexible, stretchable, and non-toxic devices that can be manufactured at an ultra-low cost. Printed electronics has emerged as a viable solution by offering not only the aforementioned features but also a high degree of customization, which enables the personalization of products and facilitates the low-cost product development process even in small batches. In the context of printed electronics, printed neuromorphic circuits offer highly customized and bespoke realization of artificial neural networks to achieve desired functionality with very small number of hardware components. However, since analog components are utilized, the performance of printed neuromorphic circuits can be influenced by various factors. In this work, we focus on three main factors that perturb the circuit output from the designed values, namely, variations due to printing errors, aging effects of printed resistors, and input variations originating from sensing uncertainty. In the described approach, these variations are taken into account during the design (training) to ensure the dependability of the printed neuromorphic circuits. With this approach, the expected accuracy and the robustness of printed neural networks can be increased by 27% and 74%, respectively. Moreover, the ablation study suggests that, aging effect and printing variation may have similar effects on the functionality of printed neural networks. In contrast, the impact of sensing uncertainty on printed neural networks is almost orthogonal to aging and printing variations.