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Printed electronics (PE) enables disruptive applications in wearables, smart sensors, and healthcare since it provides mechanical flexibility, low cost, and on-demand fabrication. The progress in PE raises trust issues in the supply chain and vulnerability to reverse engineering (RE) attacks. Recently, RE attacks on PE circuits have been successfully performed, pointing out the need for countermeasures against RE, such as camouflaging. In this article, we propose a printed camouflaged logic cell that can be inserted into PE circuits to thwart RE. The proposed cell is based on three components achieved by changing the fabrication process that exploits the additive manufacturing feature of PE. These components are optically look-alike, while their electrical behaviors are different, functioning as a transistor, short, and open. The properties of the proposed cell and standard PE cells are compared in terms of voltage swing, delay, power consumption, and area. Moreover, the proposed camouflaged cell is fabricated and characterized to prove its functionality. Furthermore, numerous camouflaged components are fabricated, and their (in)distinguishability is assessed to validate their optical similarities based on the recent RE attacks on PE. The results show that the proposed cell is a promising candidate to be utilized in camouflaging PE circuits with negligible overhead.
Neuromorphic computing systems have demonstrated many advantages for popular classification problems with significantly less computational resources. We present in this paper the design, fabrication and training of a programmable neuromorphic circuit, which is based on printed electrolytegated field-effect transistor (EGFET). Based on printable neuron architecture involving several resistors and one transistor, the proposed circuit can realize multiply-add and activation functions. The functionality of the circuit, i.e. the weights of the neural network, can be set during a post-fabrication step in form of printing resistors to the crossbar. Besides the fabrication of a programmable neuron, we also provide a learning algorithm, tailored to the requirements of the technology and the proposed programmable neuron design, which is verified through simulations. The proposed neuromorphic circuit operates at 5V and occupies 385mm 2 of area.
Advances in printed electronics (PE) enables new applications, particularly in ultra-low-cost domains. However, achieving high-throughput printing processes and manufacturing yield is one of the major challenges in the large-scale integration of PE technology. In this article, we present a programmable printed circuit based on an efficient printed lookup table (pLUT) to address these challenges by combining the advantages of the high-throughput advanced printing and maskless point-of-use final configuration printing. We propose a novel pLUT design which is more efficient in PE realization compared to existing LUT designs. The proposed pLUT design is simulated, fabricated, and programmed as different logic functions with inkjet printed conductive ink to prove that it can realize digital circuit functionality with the use of programmability features. The measurements show that the fabricated LUT design is operable at 1 V.