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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.
Embedded Analog Physical Unclonable Function System to Extract Reliable and Unique Security Keys
(2020)
Internet of Things (IoT) enabled devices have become more and more pervasive in our everyday lives. Examples include wearables transmitting and processing personal data and smart labels interacting with customers. Due to the sensitive data involved, these devices need to be protected against attackers. In this context, hardware-based security primitives such as Physical Unclonable Functions (PUFs) provide a powerful solution to secure interconnected devices. The main benefit of PUFs, in combination with traditional cryptographic methods, is that security keys are derived from the random intrinsic variations of the underlying core circuit. In this work, we present a holistic analog-based PUF evaluation platform, enabling direct access to a scalable design that can be customized to fit the application requirements in terms of the number of required keys and bit width. The proposed platform covers the full software and hardware implementations and allows for tracing the PUF response generation from the digital level back to the internal analog voltages that are directly involved in the response generation procedure. Our analysis is based on 30 fabricated PUF cores that we evaluated in terms of PUF security metrics and bit errors for various temperatures and biases. With an average reliability of 99.20% and a uniqueness of 48.84%, the proposed system shows values close to ideal.
Oxide semiconductors have the potential to increase the performance of inkjet printed microelectronic devices such as field-effect transistors (FETs), due to their high electron mobilities. Typical metal oxides are n-type semiconductors, while p-type oxides, although realizable, exhibit lower carriermobilities. Therefore, the circuit design based on oxide semiconductors is mostly in n-type logic only. Here we present an inkjet printed pn-diode based on p- and n-type oxide semiconductors.Copper oxide or nickel oxide is used as p-typesemiconductor whereas n-typesemiconductor is realized with indium oxide. Themeasurements show that the pn-diodes operate in the voltage window typical for printed electronics and the emission coefficient is 1.505 and 2.199 for the copper oxide based and nickel oxidebased pn-diode, respectively.Furthermore, a pn-diode model is developed and integrable into a circuit simulator.