Search Results - jianhua+(joshua)+yang

                                            

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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.