Cross-point arrays and methods of updating values of the same include input resistive processing units (RPUs), each having a settable resistance, each connected to a common node. Output RPUs each have a settable resistance and are each connected to the common node. An update switch is configured to connect an update voltage to the common node.

An analog content addressable memory (aCAM) that enables parallel searching of analog ranges of values and generates analog outputs that quantify matches between input data and stored data is disclosed. The input data can be compared with the stored data, and the input data can be determined to match the stored data based on a value associated with the input data being within a range associated with the stored data. The aCAM can generate an analog output that represents a number of matches and a number of mismatches between the input data and the stored data. Based on the analog output, whether the input data matches the stored data and a degree to which the input data matches the stored data can be determined.

A memory includes: a first electrode comprising a top boundary and a sidewall; a resistive material layer, disposed above the first electrode, that comprises at least a first portion and a second portion coupled to a first end of the first portion, wherein the resistive material layer presents a variable resistance value; and a second electrode disposed above the resistive material layer.

A memory unit, array and operation method thereof are provided. The memory unit includes at least one P-type driver having a first end coupled to a power source, a second end and a control end coupled to a word line; a memory cell having a first end coupled to the second end of the P-type driver, and a second end coupled to a bit line.

Systems and methods are provided for implementing a hardware accelerator. The hardware accelerator emulates a neural network, and includes a memristor crossbar array, and a non-linear filter. The memristor crossbar array can be programmed to calculate node values of the neural network. The nodes values can be calculated in accordance with rules to reduce an energy function associated with the neural network. The non-linear filter is coupled to the memristor crossbar array and programmed to harness noise signals that may be present in analog circuitry of the hardware accelerator. The noise signals can be harnessed such that the energy function associated with the neural network converges towards a global minimum and modifies the calculated node values. In some embodiments, the non-liner filter is implemented as a Schmidt trigger comparator.

A system and method of secure communication between computing devices based on physical unclonable functions such as memories having dissolvable conductive paths is provided. The method involves enrolling a client device, the client device having a PUF such as a pristine ReRAM. The PUF is enrolled in a secure environment by reading and storing the resistances of the PUF's addressable memory cells. The cells are categorized into “rugged” and “vulnerable” categories on the basis of their resistance, the vulnerable cells being those more likely to be permanently altered during the generations of PUF responses. The rugged cells are used for the generation of PUF responses for cryptographic key generation, but the vulnerable cells may be inspected to detect unauthorized 3rd party access to the PUF.

An object of one embodiment of the present invention is to provide a semiconductor device with a novel structure in which stored data can be stored even when power is not supplied in a data storing time and there is no limitation on the number of times of writing. The semiconductor device includes a first transistor which includes a first channel formation region using a semiconductor material other than an oxide semiconductor, a second transistor which includes a second channel formation region using an oxide semiconductor material, and a capacitor. One of a second source electrode and a second drain electrode of the second transistor is electrically connected to one electrode of the capacitor.

Crossbar arrays perform analog vector-matrix multiplication naturally and provide a building block for modern computing systems. In many applications, the weights stored in the crossbar array are learned off-line and then stored on embedded devices. After the weights are learned, they do not change. Since the weights do not change in these applications, this disclosure envisions a new implementation for the processing elements of the crossbar array.

A semiconductor device is provided. The semiconductor device includes a substrate a substrate, a first electrode structure on the substrate, the first electrode structure including first insulating patterns and first electrode patterns, the first insulating patterns alternately stacked with the first electrode patterns, a second electrode pattern on a sidewall of the first electrode structure, and a data storage film on a sidewall of the second electrode pattern. The data storage film has a variable resistance.

An apparatus for performing in-memory processing includes a memory cell array of memory cells configured to output a current sum of a column current flowing in respective column lines of the memory cell array based on an input signal applied to row lines of the memory cells, a sampling circuit, comprising a capacitor connected to each of the column lines, configured to be charged by a sampling voltage of a corresponding current sum of the column lines, and a processing circuit configured to compare a reference voltage and a currently charged voltage in the capacitor in response to a trigger pulse generated at a timing corresponding to a quantization level, among quantization levels, time-sectioned based on a charge time of the capacitor, and determine the quantization level corresponding to the sampling voltage by performing time-digital conversion when the currently charged voltage reaches the reference voltage.