Abstract:
According to an embodiment of the present invention, a method of manufacturing a FET device having a set BTBT leakage and a maximum VDD includes: determining an x value in InxGa1−xAs according to the BTBT leakage and the maximum VDD, and forming a channel utilizing InxGa1−xA, wherein x is not 0.53.
Abstract:
A semiconductor device including: a substrate; a first active layer on the substrate and including a first channel between a source and a drain; a second active layer stacked on the first active layer, the second active layer including a second channel between the source and the drain; a first gate corresponding to the first channel; and a second gate electrically separated from the first gate and corresponding to the second channel.
Abstract:
A device including a stacked nanosheet field effect transistor (FET) may include a substrate, a first channel pattern on the substrate, a second channel pattern on the first channel pattern, a gate that is configured to surround portions of the first channel pattern and portions of the second channel pattern, and source/drain regions on opposing ends of the first channel pattern and second channel pattern. The first and second channel patterns may each include a respective plurality of nanosheets arranged in a respective horizontal plane that is parallel to a surface of the substrate. The nanosheets may be spaced apart from each other at a horizontal spacing distance between adjacent ones of the nanosheets. The second channel pattern may be spaced apart from the first channel pattern at a vertical spacing distance from the first channel pattern to the second channel pattern that is greater than the horizontal spacing distance.
Abstract:
A neuromorphic device for the analog computation of a linear combination of input signals, for use, for example, in an artificial neuron. The neuromorphic device provides non-volatile programming of the weights, and fast evaluation and programming, and is suitable for fabrication at high density as part of a plurality of neuromorphic devices. The neuromorphic device is implemented as a vertical stack of flash-like cells with a common control gate contact and individually contacted source-drain (SD) regions. The vertical stacking of the cells enables efficient use of layout resources.
Abstract:
A neuromorphic multi-bit digital weight cell configured to store a series of potential weights for a neuron in an artificial neural network. The neuromorphic multi-bit digital weight cell includes a parallel cell including a series of passive resistors in parallel and a series of gating transistors. Each gating transistor of the series of gating transistors is in series with one passive resistor of the series of passive resistors. The neuromorphic cell also includes a series of programming input lines connected to the series of gating transistors, an input terminal connected to the parallel cell, and an output terminal connected to the parallel cell.
Abstract:
A neuromorphic device for the analog computation of a linear combination of input signals, for use, for example, in an artificial neuron. The neuromorphic device provides non-volatile programming of the weights, and fast evaluation and programming, and is suitable for fabrication at high density as part of a plurality of neuromorphic devices. The neuromorphic device is implemented as a vertical stack of flash-like cells with a common control gate contact and individually contacted source-drain (SD) regions. The vertical stacking of the cells enables efficient use of layout resources.
Abstract:
A neuromorphic multi-bit digital weight cell configured to store a series of potential weights for a neuron in an artificial neural network. The neuromorphic multi-bit digital weight cell includes a parallel cell including a series of passive resistors in parallel and a series of gating transistors. Each gating transistor of the series of gating transistors is in series with one passive resistor of the series of passive resistors. The neuromorphic cell also includes a series of programming input lines connected to the series of gating transistors, an input terminal connected to the parallel cell, and an output terminal connected to the parallel cell.
Abstract:
A method provides a gate structure for a plurality of components of a semiconductor device. A silicate layer is provided. In one aspect, the silicate layer is provided on a channel of a CMOS device. A high dielectric constant layer is provided on the silicate layer. The method also includes providing a work function metal layer on the high dielectric constant layer. A low temperature anneal is performed after the high dielectric constant layer is provided. A contact metal layer is provided on the work function metal layer.
Abstract:
A hardware cell and method for performing a digital XNOR of an input signal and weights are described. The hardware cell includes input lines, a plurality of pairs of magnetic junctions, output transistors and at least one selection transistor coupled with the output transistors. The input lines receive the input signal and its complement. The magnetic junctions store the weight. Each magnetic junction includes a reference layer, a free layer and a nonmagnetic spacer layer between the reference layer and the free layer. The free layer has stable magnetic states and is programmable using spin-transfer torque and/or spin-orbit interaction torque. The first magnetic junction of a pair receives the input signal. The second magnetic junction of the pair receives the input signal complement. The output transistors are coupled with the magnetic junctions such that each pair of magnetic junctions forms a voltage divider. The output transistors form a sense amplifier.
Abstract:
A neuromorphic weight cell (NWC) including a resistor ladder including a plurality of resistors connected in series, and a plurality of shunting nonvolatile memory (NVM) elements, each of the shunting NVM elements being coupled in parallel to a corresponding one of the resistors.