Memristor - Potential Applications

Potential Applications

Williams' solid-state memristors can be combined into devices called crossbar latches, which could replace transistors in future computers, taking up a much smaller area.

They can also be fashioned into non-volatile solid-state memory, which would allow greater data density than hard drives with access times potentially similar to DRAM, replacing both components. HP prototyped a crossbar latch memory using the devices that can fit 100 gigabits in a square centimeter, and has designed a highly scalable 3D design (consisting of up to 1000 layers or 1 petabit per cm3). In May 2008 HP reported that its version of the memristor is currently about one-tenth the speed of DRAM. The devices' resistance would be read with alternating current so that the stored value would not be affected. In May 2012 it was reported that speeds had been improved to as fast as 90 nanoseconds if not faster, approximately one hundred times faster than contemporaneous flash memory, while using one hundredth the energy.

Some patents related to memristors appear to include applications in programmable logic, signal processing, neural networks, control systems, reconfigurable computing, Brain-computer interfaces, and RFID. Memristive devices can be potentially used for stateful logic implication, allowing a replacement for CMOS-based logic computation. Several early works in this direction are reported.

In 2009, a simple electronic circuit consisting of an LC network and a memristor was used to model experiments on adaptive behavior of unicellular organisms. It was shown that the electronic circuit subjected to a train of periodic pulses learns and anticipates the next pulse to come, similarly to the behavior of slime molds Physarum polycephalum where the viscosity of channels in the cytoplasm respond to periodic changes of environment. Such a learning circuit may find applications, e.g., in pattern recognition. The DARPA’s SyNAPSE project has funded HP Labs, in collaboration with the Boston University Neuromorphics Lab, to develop neuromorphic architectures which may be based on memristive systems. In 2010, Massimiliano Versace and Ben Chandler co-wrote an article describing the MoNETA (Modular Neural Exploring Traveling Agent) model. MoNETA is the first large-scale neural network model to implement whole-brain circuits to power a virtual and robotic agent compatibly with memristive hardware computations. The software used to implement MoNETA, Cog Ex Machina, has been featured on the cover page of IEEE Computer in February 2011 in a joint article by HP Labs and the Boston University Neuromorphics Lab. Application of the memristor crossbar structure in the construction of analog soft computing system is demonstrated by Farnood Merrikh-Bayat and Saeed Bagheri Shouraki. They have also shown in 2011 how memristor crossbars can be combined with fuzzy logic to create analog memristive neuro-fuzzy computing system with fuzzy input and output terminals. Learning of this system is based on the creation of fuzzy relations inspired from Hebbian learning rule.