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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.

Emerging applications in soft robotics, wearables, smart consumer products or IoT-devices benefit from soft materials, flexible substrates in conjunction with electronic functionality. Due to high production costs and conformity restrictions, rigid silicon technologies do not meet application requirements in these new domains. However, whenever signal processing becomes too comprehensive, silicon technology must be used for the high-performance computing unit. At the same time, designing everything in flexible or printed electronics using conventional digital logic is not feasible yet due to the limitations of printed technologies in terms of performance, power and integration density. We propose to rather use the strengths of neuromorphic computing architectures consisting in their homogeneous topologies, few building blocks and analog signal processing to be mapped to an inkjet-printed hardware architecture. It has remained a challenge to demonstrate non-linear elements besides weighted aggregation. We demonstrate in this work printed hardware building blocks such as inverter-based comprehensive weight representation and resistive crossbars as well as printed transistor-based activation functions. In addition, we present a learning algorithm developed to train the proposed printed NCS architecture based on specific requirements and constraints of the technology.

Modern society is striving for digital connectivity that demands information security. As an emerging technology, printed electronics is a key enabler for novel device types with free form factors, customizability, and the potential for large-area fabrication while being seamlessly integrated into our everyday environment. At present, information security is mainly based on software algorithms that use pseudo random numbers. In this regard, hardware-intrinsic security primitives, such as physical unclonable functions, are very promising to provide inherent security features comparable to biometrical data. Device-specific, random intrinsic variations are exploited to generate unique secure identifiers. Here, we introduce a hybrid physical unclonable function, combining silicon and printed electronics technologies, based on metal oxide thin film devices. Our system exploits the inherent randomness of printed materials due to surface roughness, film morphology and the resulting electrical characteristics. The security primitive provides high intrinsic variation, is non-volatile, scalable and exhibits nearly ideal uniqueness.

Oxide semiconductors are highly promising candidates for the most awaited, next-generation electronics, namely, printed electronics. As a fabrication route for the solution-processed/printed oxide semiconductors, photonic curing is becoming increasingly popular, as compared to the conventional thermal curing method; the former offers numerous advantages over the latter, such as low process temperatures and short exposure time and thereby, high throughput compatibility. Here, using dissimilar photonic curing concepts (UV-visible light and UV-laser), we demonstrate facile fabrication of high performance In 2 O 3 field-effect transistors (FETs). Beside the processing related issues (temperature, time etc.), the other known limitation of oxide electronics is the lack of high performance p-type semiconductors, which can be bypassed using unipolar logics from high mobility n-type semiconductors alone. Interestingly, here we have found that our chosen distinct photonic curing methods can offer a large variation in threshold voltage, when they are fabricated from the same precursor ink. Consequently, both depletion and enhancement-mode devices have been achieved which can be used as the pull-up and pull-down transistors in unipolar inverters. The present device fabrication recipe demonstrates fast processing of low operation voltage, high performance FETs with large threshold voltage tunability.

Systems biology envisions that the application of complex system engineering approaches to cell signaling molecular networks can lead to novel understandings of complex human disorders. In this paper we show that by developing biologically-driven vulnerability assessment methods, the vulnerability of complex signaling networks to the dysfunction of each molecule can be determined. We have analyzed signaling networks that regulate mitosis and the activity of the transcription factor CREB. Our results indicate that biologically-relevant critical components of intracellular molecular networks can be identified using the proposed systems biology/fault diagnosis engineering technique. The application of this approach can improve our physiological understanding of the functionality of biological systems, can be used as a tool to identify novel genes associated with complex human disorders, and ultimately, has the potential to find the most prominent targets for drug discovery.

The application of complex system engineering approaches to cell signaling networks should lead to novel understandings and, subsequently, new treatments for complex disorders. In the area of circuit fault diagnosis engineering, there are various methods to identify the defective or vulnerable components of complex digital electronic circuits. In biological systems, however, knowledge is limited regarding the vulnerability of interconnected signaling pathways to the dysfunction of each specific molecule. By developing proper biologically driven digital vulnerability assessment methods, the vulnerability of complex signaling networks to the possible dysfunction of each molecule can be determined. To show the utility of this approach, we analyzed three well-characterized signaling networks--a cellular network that regulates the activity of caspase3, a network that regulates the activity of p53, and a central nervous system network that regulates the activity of the transcription factor CREB (adenosine 3',5'-monophosphate response element-binding protein). We found important differences among the vulnerability values of different molecules. Most of the identified highly vulnerable molecules are functionally related and known key regulators of these networks. Experimental data confirmed the ability of digital vulnerability assessment to correctly predict key regulators in the CREB network. Because this approach may provide insight into key molecules that contribute to human diseases, it may aid in the identification of critical targets for drug development.