A semiconductor device that can perform product-sum operation with low power is provided. The semiconductor device includes a switching circuit. The switching circuit includes first to fourth terminals. The switching circuit has a function of selecting one of the third terminal and the fourth terminal as electrical connection destination of the first terminal, and selecting the other of the third terminal and the fourth terminal as electrical connection destination of the second terminal, on the basis of first data. The switching circuit includes a first transistor and a second transistor each having a back gate. The switching circuit has a function of determining a signal-transmission speed between the first terminal and one of the third terminal and the fourth terminal and a signal-transmission speed between the second terminal and the other of the third terminal and the fourth terminal on the basis of potentials of the back gates. The potentials are determined by second data. When signals are input to the first terminal and the second terminal, a time lag between the signals output from the third terminal and the fourth terminal is determined by the first data and the second data.

In-vehicle detection system includes nonvolatile memory, a controller (SoC) that reads and writes data from and in nonvolatile memory, and detector that outputs detection information to SoC. SoC changes a control signal of nonvolatile memory in accordance with the output of detector.

A system may include a memory array for VMM and includes a matrix of devices. The devices may be configured to receive a programming signal to program a weight to store a matrix of weights. The devices may be configured to receive a digital signal representative of a vector of input bits. The devices may generate an analog output signal by individually multiplying input bits by a corresponding weight. The system may include multiple ADCs electrically coupled to a corresponding device. Each ADC may be configured to convert a corresponding analog output signal to a digital signal based on a current level of the corresponding analog output signal. The system may include registers electrically coupled to a corresponding ADC configured to shift and store an output vector of bits of a corresponding digital output signal based on an order of the vector of input bits received by the corresponding device.

The present invention discloses a memristor array, comprising metal wires and memristors; the metal wires are arranged laterally and vertically; a memristor is arranged at the intersection of every two metal wires; the connection/disconnection of the metal wires is judged according to the resistance values of the memristors; and an adder is constituted according to the resistance value states of the memristors. The present invention provides a memristor-CMOS hybrid multiplication core circuit, in which one input of multiplication can be stored in a memristor network, one part of operation is completed in a memory network, the other part of operation is completed through a CMOS circuit, thereby reducing frequent data calls by half, and the power consumption of the CMOS circuit is further reduced by reducing competitive adventure in the operation process, thereby greatly reducing the overall energy consumption.

A neural network circuit is described that includes a first sample-and-hold circuit, a reference voltage generation circuit, a first comparator circuit, and a first output circuit. The first sample-and-hold circuit generates a first analog voltage based on a first output current output by a first neural network computation array. The reference voltage generation circuit generates a reference voltage based on a first control signal. The first comparator circuit is connected to the first sample-and-hold circuit and the reference voltage generation circuit, and outputs a first level signal based on the first analog voltage and the reference voltage. The first output circuit samples the first level signal based on a second control signal, and outputs a first computation result that meets the first computation precision.

An electronic device comprising a semiconductor memory is provided. The semiconductor memory includes a substrate including a cell region and a peripheral circuit region, the cell region including a first cell region and a second cell region, the first cell region being disposed closer to the peripheral circuit region than the second cell region; second lines disposed over the first lines and extending in a second direction crossing the first direction; memory cells positioned at intersections between the first lines and the second lines in the cell region; a first insulating layer positioned between the first lines, between the second line, or both, in the first cell region; and a second insulating layer positioned between the first lines and between the second lines in the second cell region. A dielectric constant of the first insulating layer is smaller than that of the second insulating layer.

In one example in accordance with the present disclosure a device is described. The device includes at least two memristive cells. Each memristive cell includes a memristive element to store one component of a complex weight value. The device also includes a real input multiplier coupled to the memristive element to multiply an output signal of the memristive element with a real component of an input signal. An imaginary input multiplier of the device is coupled to the memristive element to multiply the output signal of the memristive element with an imaginary component of the input signal.

A method for fabricating memory device is provided. The method includes forming a transistor on a substrate. Further, a contact structure is formed on a source/drain region of the transistor. A conductive layer is formed on the contact structure. Four memory structures are formed on the conductive layer to form a quadrilateral structure.

A nonvolatile memory device according to an embodiment includes a substrate having an upper surface, a gate line structure disposed over the substrate, a gate dielectric layer covering one sidewall surface of the gate line structure and disposed over the substrate, a channel layer disposed to cover the gate dielectric layer and disposed over the substrate, a bit line structure and a resistance change structure to contact different portions of the channel layer over the substrate, and a source line structure disposed in the resistance change structure. The gate line structure includes at least one gate electrode layer pattern and interlayer insulation layer pattern that are alternately stacked along a first direction perpendicular to the substrate, and extends in a second direction perpendicular to the first direction.

Methods and apparatus are disclosed for managing the storage of dynamic neural network data within bit-addressable memory devices, such phase change memory (PCM) arrays or other storage class memory (SCM) arrays. In some examples, a storage controller determines an expected amount of change within data to be updated. If the amount is below a threshold, an In-place Write is performed using bit-addressable writes via individual SET and RESET pulses. Otherwise, a modify version of an In-place Write is performed where a SET pulse is applied to preset a portion of memory to a SET state so that individual bit-addressable writes then may be performed using only RESET pulses to encode the updated data. In other examples, a storage controller separately manages static and dynamic neural network data by storing the static data in a NAND-based memory array and instead storing the dynamic data in a SCM array.