One way to move computational power beyond the limits imposed by Moore's Law is to build computer architectures that mimic the brain. Indeed the brain is able to perform certain classes of complex computations, such as pattern recognition, very efficiently and using low power in spite of its inherent low speed. A novel device that exhibits some of the useful properties of the synapses found in the brain, such as ultra low-power switching, is explored in this proposal. Its working mechanism will be studied in detail and arrays of devices will be fabricated and miniaturized in order to provide a first step towards brain-like integrated circuits. Neuromorphic electronics, as these devices are often called, are a growing area of interest, therefore the research developed in this award has the potential to impact the future of the microelectronics industry and to produce a workforce trained for this emerging area. Finally, the optical properties of the device, which changes color upon operation, will allow to develop an outreach activity connecting art and science with the ultimate goal of attracting a more diverse student population to the area of materials science of electronic devices. A new device based on polymeric semiconductors that mimics the functions of synapses, called ENODe (electrochemical neuromorphic organic device), will be studied. ENODes with ultra-low switching energy (<10 pJ), sub-mV switching voltage, retaining >500 individual states in a non-volatile fashion were demonstrated. The physical origin of such low switching energies and voltages will be investigated with the goal of optimizing the architecture of the ENODe. Furthermore, ENODes will be fabricated with an array of different materials that may enable a higher level of integration making use of solid-state technology to increase the switching speed beyond 1 kHz into the MHz range. Finally, using the same process used in traditional microelectronics, arrays of ENODes will be fabricated to test the reproducibility and the scaling laws of these new devices.

Agency
National Science Foundation (NSF)
Institute
Division of Electrical, Communications and Cyber Systems (ECCS)
Application #
1739795
Program Officer
Lawrence Goldberg
Project Start
Project End
Budget Start
2017-08-15
Budget End
2020-07-31
Support Year
Fiscal Year
2017
Total Cost
$210,087
Indirect Cost
Name
Stanford University
Department
Type
DUNS #
City
Stanford
State
CA
Country
United States
Zip Code
94305