Abstract: An operating circuit and an operating method of a resistive random-access memory are provided, the operating circuit includes: at least one capacitance connected in series with the resistive random-access memory, so that the resistive random-access memory is grounded through the at least one capacitance. The operating method includes: connecting at least one capacitance in series with a resistive random-access memory, so that the resistive random-access memory is grounded through the capacitance; applying a forming pulse voltage or a set pulse voltage on the resistive random-access memory to achieve a forming operation or a set operation of the resistive random-access memory.

Abstract: The present disclosure provides a complementary phototransistor pixel unit, a sensing and computing array structure and an operation method thereof. The complementary phototransistor pixel unit includes: a first photoelectric field effect transistor, which is a photoelectric field effect transistor based on an ultra-thin body and buried oxide layer; and a second photoelectric field effect transistor, the second photoelectric field effect transistor is a photoelectric field effect transistor based on an ultra-thin body and buried oxide layer, each of the first photoelectric field effect transistor and the second photoelectric field effect transistor is four-end device and has a gate electrode G, a source electrode S, a drain electrode D, and a well base electrode B, and the source electrode S or drain electrode D of the first photoelectric field effect transistor is connected to the source electrode S or drain electrode D of the second photoelectric field effect transistor.

Abstract: The present disclosure provides a method and an apparatus for operating an in-memory computing architecture applied to a neural network and a device, the method includes: generating a mono-pulse input signal based on discrete time coding; inputting the mono-pulse input signal into a memory array of the in-memory computing architecture to generate a bit line current signal corresponding to the memory array; and controlling a neuron circuit of the in-memory computing architecture to output a mono-pulse output signal based on discrete time coding according to the bit line current signal, wherein the mono-pulse output signal is configured as a mono-pulse input signal of a memory array of the next layer of neural network in the next in-memory computing cycle.

Abstract: The present disclosure provides an image sensing computing unit and its operating method, an image sensing computer and an electronic device. Among them, the image sensing computing unit includes a first photosensitive unit and a second photosensitive unit. The second photosensitive unit is connected in series with the first photosensitive unit. The changing direction of the first threshold voltage of the first photosensitive unit when receiving light is opposite to the changing direction of the second threshold voltage of the second photosensitive unit when receiving light, so as to implement an in-situ logical operation between light input signals.

Abstract: A system and a method for compressing an image based on a FLASH in-memory computing array are provided. The system includes: a convolutional neural network for encoding of the FLASH in-memory computing array, a convolutional neural network for decoding based on the FLASH in-memory computing array, and a quantization module; the convolutional neural network for encoding based on the FLASH in-memory computing array is configured to encode an original image to obtain a feature image; the quantization module is configured to quantize the feature image to obtain a quantized image; the convolutional neural network for decoding based on the FLASH in-memory computing array is configured to decode the quantized image to obtain a compressed image.

Abstract: A method of solving a partial differential equation based on a non-volatile memory array includes converting a to-be-solved partial differential equation into an iterative relation, selecting a reusable sub-matrix cell from the iterative coefficient matrix, and storing the sub-matrix cell in the memory array, extracting an input vector from the iteration vector, inputting the input vector into the memory array, updating a portion of the iteration vector by adding an obtained output vector to a portion of the constant vector, extracting the input vector from an updated iteration vector again, and inputting the input vector into the memory array until all elements of the iteration vector are updated to obtain an iteration vector for a next iteration, and ending the iteration when a preset number of iterations is reached or an error is less than a preset range.

Abstract: A spiking convolutional neural network based on a FLASH storage and computing array, including: a sampling module, a FLASH-based storage and computing array and a corresponding neuron module, and a counter module; the sampling module is used to sample an input image to obtain an input spike; the FLASH-based storage and computing array stores a weight matrix, and is used to perform a vector matrix multiplying operation on the input spike and the weight matrix, and an operation result is output in a form of current; the neuron module is used to integrate the operation result of the FLASH-based storage and computing array so as to generate an output spike; the counter module is used to count a number of spikes generated by the neuron module of an output layer, and determine the number of spikes of the neuron module with a largest number of spikes as a recognition result.

Abstract: A deep neural network based on analog FLASH computing array, includes a number of computing arrays, a number of subtractors, a number of activation circuit units and a number of integral-recognition circuit units. The computing array includes a number of computing units, a number of word lines, a plurality number of bit lines and a number of source lines. Each of the computing units includes a FLASH cell. The gate electrodes of the FLASH cells in the same column are connected to the same word line. The source electrodes of the FLASH cells in the same column are connected to the same source line, and the drain electrodes of the FLASH cells in the same row are connected to the same bit line. Each of the subtractors includes a positive terminal, a negative terminal and an output terminal.

Abstract: A system and a method for compressing an image based on a FLASH in-memory computing array are provided. The system includes: a convolutional neural network for encoding of the FLASH in-memory computing array, a convolutional neural network for decoding based on the FLASH in-memory computing array, and a quantization module; the convolutional neural network for encoding based on the FLASH in-memory computing array is configured to encode an original image to obtain a feature image; the quantization module is configured to quantize the feature image to obtain a quantized image; the convolutional neural network for decoding based on the FLASH in-memory computing array is configured to decode the quantized image to obtain a compressed image.

Abstract: An operating circuit and an operating method of a resistive random-access memory are provided, the operating circuit includes: at least one capacitance connected in series with the resistive random-access memory, so that the resistive random-access memory is grounded through the at least one capacitance. The operating method includes: connecting at least one capacitance in series with a resistive random-access memory, so that the resistive random-access memory is grounded through the capacitance; applying a forming pulse voltage or a set pulse voltage on the resistive random-access memory to achieve a forming operation or a set operation of the resistive random-access memory.

Abstract: The present disclosure relates to the field of semiconductor integrated circuits and manufacturing technologies thereof, and discloses a method and device for realizing a convolution operation based on an NOR flash storage structure. The NOR flash array has a structure of an array composed of a plurality of NOR flash cells. The convolution operation method includes: storing elements of a convolution kernel matrix into the NOR flash cells; converting elements of an input matrix into voltages and applying the voltages to gate terminals of the NOR flash cells; applying a driving voltage to source terminals of the NOR flash cells; and collecting, via drain terminals of the NOR flash cells, current values of each column to obtain a convolution operation result.

Abstract: The present disclosure provides a data search system and a data search method for determining whether there is stored information data matched with query information data in a storage circuit. The data search system comprises a storage circuit, a control circuit, and a feature extraction circuit, wherein the storage circuit comprises at least one storage unit which comprises a memristor crossbar array, a read/write unit, a decoder and a multiplexer, and the feature extraction circuit is configured to extract feature values of the query data. In the data search method, both the data matching process and the data storage process are performed in the memristor crossbar array under the control of the control circuit, which thus largely reduces the amount of data transmission while greatly improving the speed of data search using the characteristics of parallel calculation of the memristor crossbar array.

Abstract: The present disclosure relates to the field of semiconductor integrated circuits and manufacturing technologies thereof, and discloses a method and device for realizing a convolution operation based on an NOR flash storage structure. The NOR flash array has a structure of an array composed of a plurality of NOR flash cells. The convolution operation method includes: storing elements of a convolution kernel matrix into the NOR flash cells; converting elements of an input matrix into voltages and applying the voltages to gate terminals of the NOR flash cells; applying a driving voltage to source terminals of the NOR flash cells; and collecting, via drain terminals of the NOR flash cells, current values of each column to obtain a convolution operation result.

Abstract: The present disclosure provides a data search system and a data search method for determining whether there is stored information data matched with query information data in a storage circuit. The data search system comprises a storage circuit, a control circuit, and a feature extraction circuit, wherein the storage circuit comprises at least one storage unit which comprises a memristor crossbar array, a read/write unit, a decoder and a multiplexer, and the feature extraction circuit is configured to extract feature values of the query data. In the data search method, both the data matching process and the data storage process are performed in the memristor crossbar array under the control of the control circuit, which thus largely reduces the amount of data transmission while greatly improving the speed of data search using the characteristics of parallel calculation of the memristor crossbar array.

Abstract: The present disclosure discloses a full adder based on resistive-switching devices and an operation method thereof. A multi-bit full adder circuit is constituted by using a cross-bar array of resistive-switching devices, wherein data of standard sums is stored on the principle diagonal of the cross-bar array in a nonvolatile manner, and carry data is stored in adjacent units on both sides of the principle diagonal. The carry data is stored according to whether the storage loop (crosstalk loop) is turned on. With the present disclosure, the multi-bit full adder circuit is significantly simplified. Thereby, additional circuits for generating a carry signal are reduced, the circuit delay and chip area are decreased, and the adder has an ability of nonvolatile storage.

Abstract: The present invention discloses a three-dimensional one-time-programmable memory (3D-OTP) comprising an off-die address/data-translator (A/D-translator). It comprises at least a 3D-array die and at least a peripheral-circuit die. At least an A/D-translator of the 3D-OTP arrays is located on the peripheral-circuit die instead of the 3D-array die. The A/D-translator converts at least an address and/or data between logic and physical spaces.

Abstract: The present disclosure discloses a full adder based on resistive-switching devices and an operation method thereof. A multi-bit full adder circuit is constituted by using a cross-bar array of resistive-switching devices, wherein data of standard sums is stored on the principle diagonal of the cross-bar array in a nonvolatile manner, and carry data is stored in adjacent units on both sides of the principle diagonal. The carry data is stored according to whether the storage loop (crosstalk loop) is turned on. With the present disclosure, the multi-bit full adder circuit is significantly simplified. Thereby, additional circuits for generating a carry signal are reduced, the circuit delay and chip area are decreased, and the adder has an ability of nonvolatile storage.

Abstract: The present invention discloses a small-grain three-dimensional memory (3D-MSG). Each of its memory cells comprises a thin-film diode with critical dimension no larger than 40 nm. The thin-film diode comprises at least a small-grain material, whose grain size G is substantially smaller than the diode size D. The small-grain material is preferably a nano-crystalline material or an amorphous material. The critical dimension f of the small-grain diode is smaller than the critical dimension F of the single-crystalline transistor.

Abstract: A method for measuring data retention characteristic of an RRAM device includes: a) controlling a temperature of a sample stage to maintain the RRAM device at a predetermined temperature; b) setting the RRAM device to a high-resistance state or a low-resistance state; c) measuring data retention time by applying a predetermined voltage to the RRAM device so that a resistive state failure of the RRAM device occurs; d) repeating the steps a)-c) to perform a plurality of measurements; e) calculating a resistive state failure probability F(t) of the RRAM device from the data retention time in the plurality of measurements; and f) fitting the resistive state failure probability F(t), and calculating predicted data retention time tE by using parameters obtained from the fitting. The data retention time of the RRAM device may be predicted by combining voltage acceleration and temperature acceleration.

Abstract: A resistive switching memory device and a method for operating the same are disclosed. The device includes a plurality of resistive switching memory units arranged in a matrix, each of which includes a switching element and a resistive switching device, and the switching element being connected to a word line at its control terminal, to the resistive switching device at one terminal, and to a bit line at the other terminal; a word line decoder adapted to decode an input address signal to switch on the switching element in at least one of resistive switching memory units; and a driving circuit adapted to apply a voltage pulse whose front edge changes slowly across the resistive switching device by the bit line synchronously with the switching-on of the switching element.