Low-voltage artificial neuron using feedback engineered insulator-to-metal-transition devices
| Autor: | Shriram Ramanathan, Liliana Stan, K.V.L.V Achari, Annadi, Jianqiang Lin, Sushant Sonde, Changyao Chen, Supratik Guha |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
010302 applied physics
Engineering business.industry Material element Insulator (electricity) 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Temperature measurement 0103 physical sciences Scalability Electronic engineering Artificial neuron 0210 nano-technology business Scaling Low voltage Voltage |
| Zdroj: | 2016 IEEE International Electron Devices Meeting (IEDM). |
| DOI: | 10.1109/iedm.2016.7838541 |
| Popis: | We demonstrate a solid-state spiking artificial neuron based upon an insulator-to-metal (IMT) transition material element that operates at an unprecedented low voltage (0.8 V). We have developed a general coupled electrical-thermal device model for IMT based devices to accurately predict experimental outcomes. From the experiment and simulation, we show that voltage scalability to sub 0.3 V is possible by scaling of the IMT based neuron. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|