Artificial Neurons on Flexible Substrates: A Fully Printed Approach for Neuromorphic Sensing

• Published in: Sensors (Basel, Switzerland) Switzerland), 2022-05, Vol.22 (11), p.4000
• Main Authors: Singaraju, Surya A, Weller, Dennis D, Gspann, Thurid S, Aghassi-Hagmann, Jasmin, Tahoori, Mehdi B
• Format: Article
• Language: English
• Subjects: Ablation; artificial neural networks; Conductors; Design; Electrolytes; Electronic circuits; Electronics; flexible and functional inks; Graphene; Ink jet printers; Internet of Things; Medical equipment; Neural networks; Neuromorphic computing; neuromorphic sensing and computing; Neurons; Polyvinyl alcohol; printed electronics; Sensors; Smart sensors; Software; Tensile strain; Thickness; Transistors
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22114000

Printed electronic devices have demonstrated their applicability in complex electronic circuits. There is recent progress in the realization of neuromorphic computing systems (NCSs) to implement basic synaptic functions using solution-processed materials. However, a fully printed neuron is yet to be realised. We demonstrate a fully printed artificial neuromorphic circuit on flexible polyimide (PI) substrate. Characteristic features of individual components of the printed system were guided by the software training of the NCS. The printing process employs graphene ink for passive structures and In2O3 as active material to print a two-input artificial neuron on PI. To ensure a small area footprint, the thickness of graphene film is tuned to target a resistance and to obtain conductors or resistors. The sheet resistance of the graphene film annealed at 300 °C can be adjusted between 200 Ω and 500 kΩ depending on the number of printed layers. The fully printed devices withstand a minimum of 2% tensile strain for at least 200 cycles of applied stress without any crack formation. The area usage of the printed two-input neuron is 16.25 mm2, with a power consumption of 37.7 mW, a propagation delay of 1 s, and a voltage supply of 2 V, which renders the device a promising candidate for future applications in smart wearable sensors.