Learn More
Background Carbon Nanotube Field Effect Transistors (CNTFETs) have high charge sensitivity at room temperature [1]. By using this sensitivity, some nonvolatile memory devices have been demonstrated with charge trapping in SiO 2 gate insulator [2, 3]. Besides, a new design of synapse-like circuit requires a multi-level nonvolatile memory [4]. For this(More)
We present an original method to implement neuro-inspired supervised learning for a synaptic array based on carbon nanotube devices. The device characteristics required to implement on chip learning within a crossbar of carbon nanotube field effect transistors (CNTFETs) as synaptic arrays were experimentally demonstrated and accurately modeled through a(More)
As the fabrication cost of CMOS mask increases exponentially while the technology is approaching its physical limits, research interest focuses on emerging technologies and alternative architectures. Non-volatile components are considered as possible alternative technologies and neural networks constitute an interesting framework. Here, we present a(More)
Nanoelectronic circuit design flow is based on device description through the compact models available in the designer device library. We have developed a compact model for the Optically-Gated CNTFET by investigating the trapping-detrapping of electron effects in the device. This compact model represents an important enhancement of conventional CNTFET(More)
  • 1