We report on the detailed electrical investigation of all-inkjet-printed thin-film transistor (TFT) arrays focusing on TFT failures and their origins. The TFT arrays were manufactured on flexible polymer substrates in ambient condition without the need for cleanroom environment or inert atmosphere and at a maximum temperature of 150 °C. Alternative manufacturing processes for electronic devices such as inkjet printing suffer from lower accuracy compared to traditional microelectronic manufacturing methods. Furthermore, usually printing methods do not allow the manufacturing of electronic devices with high yield (high number of functional devices). In general, the manufacturing yield is much lower compared to the established conventional manufacturing methods based on lithography. Thus, the focus of this contribution is set on a comprehensive analysis of defective TFTs printed by inkjet technology. Based on root cause analysis, we present the defects by developing failure categories and discuss the reasons for the defects. This procedure identifies failure origins and allows the optimization of the manufacturing resulting finally to a yield improvement.

A fully-unsupervised learning algorithm for reaching self-organization in neuromorphic architectures is provided in this work. We experimentally demonstrate spike-timing dependent plasticity (STDP) in Oxide-based Resistive Random Access Memory (OxRAM) devices, and propose a set of waveforms in order to induce symmetric conductivity changes. An empirical model is used to describe the observed plasticity. A neuromorphic system based on the tested devices is simulated, where the developed learning algorithm is tested, involving STDP as the local learning rule. The design of the system and learning scheme permits to concatenate multiple neuromorphic layers, where autonomous hierarchical computing can be performed.

Biological and artificial neural networks are meant to benefit from high resilience to noise, and to display great performance when learning tasks are considered. The advantage of a hardware-based artificial neural network, against its software counterpart, is that it is able to achieve similar results with a significant reduction of size, time and power consumption. In this work, the first RRAM-based self-organizing and topographic neuromorphic system is proposed. An automatic characterization setup has been developed for the study of our RRAM devices response to pulse-programming. An extension to a static compact model for non-linear memristive devices is provided. This extension allows including variability effects and has been used for performing crossbar arrays simulations. Inspiration in the biological mechanisms involved in sensory processing is taken for adapting the self-organizing map algorithm, commonly used in artificial intelligence, to achieve topographical organization. Results support that our RRAM-based neuromorphic system has significant tolerance to device variability.