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Neuromorphic computing, systems designed to mimic the biological nervous system, require far less power than current computer processors. The increased efficiency makes feasible artificial intelligence applications for smaller, hand-held devices (e.g. smartphones, tablets).  To this end, UMass inventors have designed hardware components that mimic neuronal synapses (Figure A). Specifically, diffusive Ag-in-oxide memristors show a temporal response during and after stimulation similar to that of a biological synapse. The novel diffusive memristor and its synapse-like dynamics enable a direct emulation of both short- and long-term plasticity of biological synapses and represent a major advancement in a hardware implementation for neuromorphic computing.

True random number generators (TRNGs) are used in cryptography and cyber security, and are increasingly important in an interconnected world at risk from cyber threats. Hardware random number generators use physical variables, such as thermal noise or the photoelectric effect, as sources of randomness in number generation. However, they suffer from drawbacks such as scalability, circuit complexity, and reliance on post-processing.

 

Here, Dr. Qiangfei Xia and Dr. Joshua Yang have invented a memristor-based TRNG. It is the first memristor-based TRNG to pass NISTSs 15 randomness tests without the need for post processing. In operation, the device switches to a low resistance state under a voltage pulse after a random delay time, and relaxes back to a high resistance state spontaneously once the electrical bias is removed. These are the sources of randomness for the TRNG. Memristor-based TRNGs offer a compact, fast, and energy-efficient design.

Resistance switching devices, also known as memristive devices, represent the next generation in computing. With a typical metal/insulator/metal structure, memristors change resistance based on past current flow and retain this new resistance even when turned off. This allows memristors to store data without needing constant power like in traditional computer memory. Memristors have other desirable properties such as low power consumption, fast switching speed, and great cycling ability. These properties open up next generation computing applications in non-volatile memory, reconfigurable switches, bio-inspired neuromorphic computing, and radiofrequency switches.

 

Here, Dr. Qiangfei Xia and Dr. Joshua Yang have invented a Ta/HfO2/Pt memristive device that can be used for multilevel memory and for neuromorphic computing. The device exhibits bipolar resistive switching with low programming voltage (~1.5 V), high endurance (100 billion cycles), and long data retention time (37,000 years at 85 C). The device can be programmed to multiple resistance states with long retention time for each individual state. Finally, spike dependent plasticity (STDP) is also demonstrated for this device. The device also has the advantage of being able to be fabricated using traditional CMOS materials and techniques.

2-dimensional (2D) materials are characterized as being one or two atoms thick. Graphene (image above) is by far the best known 2D material. However, there are many other 2D materials with attractive properties (e.g. hexagonal boron nitrides (hBN), transition metal dichalcogenides, etc.). Due to their superior physical properties, graphene and related 2D materials have the potential to revolutionize many industries, enable development of new devices, and provide new functionalities to existing technologies. In short, these materials have the potential to be disruptive and pervasive. Despite their overwhelming promise, however, industrial scale manufacture of these materials is not yet a reality, due in part to an inability to control layer number and to print over large surface areas.

 

To address this problem, scientists at UMass Amherst have engineered a high-precision printing method that is compatible with current industrial manufacturing processes. This simple method allows single layer 2D material to be patterned, transferred and printed onto a substrate, enabling the fabrication of novel 2D heterostructure devices. Thus, this method will facilitate the assembly of novel devices and enable large-scale manufacture of devices with designed properties.

Memristive devices are characterized by their present resistance being dependent on the current that last passed through them.  In this invention, a memristive RF switch is created by having two micro-electrodes with a small air gap, e.g., 50nm or less, between them.  When in the “off” state, the air gap between the electrodes gives the device a very high resistance.  When a “setting voltage” is applied between the electrodes, a conductive filament is self-created from one electrode, which bridges the air gap and contacts the other electrode.  The device in now the “on” state, and resistance is very low.  To turn the device off again, a “resetting voltage” having opposite polarity is applied, and the conductive filament’s connection to the other electrode is broken.

A new low power resistance random access memory (RRAM) device based on silicon materials has been invented. RRAM devices are non-volatile memory devices as well as promising candidates to replace FLASH memory and become the front runner among non-volatile memories. Instead of charge storage, RRAM uses high and low resistance as state variables. RRAM devices are attractive due to their fast switch speed, overwrite ability without erase, low power consumption, high endurance and long retention times. However, RRAM devices with low programming voltages and excellent device-to-device performance repeatability are still yet to be implemented. The current invention addresses these issues. Moreover, unlike other RRAM devices currently under development, these devices use only silicon-based materials making them compatible with CMOS technology. Altogether, these improvements make this new RRAM device an attractive option for commercial development.