Secure HfO2 based charge trap EEPROM with lifetime and data retention time modeling

Trusted computing is currently the most promising security strategy for cyber physical systems. Trusted computing platform relies on securely stored encryption keys in the on-board memory. However, research and actual cases have shown the vulnerability of the on-board memory to physical cryptographic attacks. This work proposed an embedded secure EEPROM architecture employing charge trap transistor to improve the security of storage means in the trusted computing platform. The charge trap transistor is CMOS compatible with high dielectric constant material as gate oxide which can trap carriers. The process compatibility allows the secure information containing memory to be embedded with the CPU. This eliminates the eavesdropping and optical observation. This effort presents the secure EEPROM cell, its high voltage programming control structure and an interface architecture for command and data communication between the EEPROM and CPU. The interface architecture is an ASIC based design that exclusively for the secure EEPROM. The on-board programming capability enables adjustment of programming voltages and accommodates EEPROM threshold variation due to PVT to optimize lifetime. In addition to the functional circuitry, this work presents the first model of lifetime and data retention time tradeoff for this new type of EEPROM. This model builds the bridge between desired data retention time and lifetime while producing the corresponding programming time and voltage

Understanding Quantum Technologies 2022

Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma

The Journal of Microelectronic Research

https://scholarworks.rit.edu/meec_archive/1013/thumbnail.jp

Nanoparticle devices for brain-inspired computing.

The race towards smarter and more efficient computers is at the core of our technology industry and is driven by the rise of more and more complex computational tasks. However, due to limitations such as the increasing costs and inability to indefinitely keep shrinking conventional computer chips, novel hardware architectures are needed. Brain-inspired, or neuromorphic, hardware has attracted great interest over the last decades. The human brain can easily carry out a multitude of tasks such as pattern recognition, classification, abstraction, and motor control with high efficiency and extremely low power consumption. Therefore, it seems logical to take inspiration from the brain to develop new systems and hardware that can perform interesting computational tasks faster and more efficiently. Devices based on percolating nanoparticle networks (PNNs) have shown many features that are promising for the creation of low-power neuromorphic systems. PNN devices exhibit many emergent brain-like properties and complex electrical activity under stimulation. However, so far PNNs have been studied using simple two-contact devices and relatively slow measuring systems. This limits the capabilities of PNNs for computing applications and questions such as whether the brain-like properties continue to be observed at faster timescales, or what are the limits for operation of PNN devices remain unanswered. This thesis explores the design, fabrication, and testing of the first successful multi- contact PNN devices. A novel and simple fabrication technique for the creation of working electrical contacts to nanoparticle networks is presented. Extensive testing of the multi-contact PNN devices demonstrated that electrical stimulation of multiple input contacts leads to complex switching activity. Complex switching activity exhibited different patterns of switching behaviour with events occurring on all contacts, on few contacts, or only on a single contact. The device behaviour is investigated for the first time at microsecond timescales, and it is found that the PNNs exhibit stochastic spiking behaviour that originates in single tunnel gaps and is strikingly similar to that observed in biological neurons. The stochastic spiking behaviour of PNNs is then used for the generation of high quality random numbers which are fundamental for encryption and security. Together the results presented in this thesis pave the way for the use of PNNs for brain-inspired computing and secure information processing