Abstract: The present disclosure relates to a method for producing vinyl acetate, comprising a vinyl acetate synthesis process, a vinyl acetate refining process and a separation process of vinyl acetate and ethyl acetate, including an acetic acid evaporator, an oxygen mixer, a vinyl acetate synthesis reactor, a first gas separating tower, a second gas separating tower, a recovered gas compressor, a water washing tower, an absorption tower and a desorption tower, a recycling gas compressor; the vinyl acetate refining process comprising an acetic acid tower, a crude VAC tower, and a fine VAC tower, a rectifying tower, an ethyl acetate tower, a water phase receiving tank, an extracting and rectifying tower and an ethyl acetate phase separator.

Abstract: A compilation method, a compilation apparatus suitable for an In-Memory Computing apparatus, a computing device and a storage medium. The compilation method includes: acquiring calculation information of an algorithm to be compiled; converting the algorithm to be compiled into the first intermediate representation according to the calculation information; mapping the first intermediate representation to the second intermediate representation; and compiling the algorithm to be compiled into instruction information recognized by the In-Memory Computing apparatus according to the hardware information, to make the In-Memory Computing apparatus execute the instruction information. The compilation method may compile the calculation information into instructions that may be directly executed by the In-Memory Computing apparatus, so as to realize the effect of accelerating the operations of various algorithms by using the In-Memory Computing apparatus.

Abstract: A signal processing device and a signal processing method. The signal processing device includes a receiver, a memristor array and a classifier. The receiver is configured to receive a first signal. The memristor array includes a plurality of memristor units, each of the plurality of memristor units includes a memristor, and the memristor array is configured to apply the first signal that has been received to at least one memristor unit of the plurality of memristor units and output a second signal based on a memristor resistance value distribution of the memristor array. The classifier is configured to classify the second signal outputted from the memristor array to obtain a type of the first signal.

Abstract: At least one embodiment of the present disclosure provides a reservoir computing apparatus and a data processing method. The reservoir computing apparatus includes: a signal input circuit, configured to receive an input signal; a reservoir circuit, including a plurality of reservoir sub-circuits, in which each reservoir sub-circuit includes a mask sub-circuit and a rotating neuron sub-circuit, the mask sub-circuit is configured to perform a first processing on the input signal with a first weight to obtain a first processing result, and the rotating neuron sub-circuit is configured to perform a second processing on the first processing result to obtain a second processing result; and an output layer circuit, configured to multiply a plurality of second processing results by a second weight matrix to obtain a third processing result. The reservoir computing apparatus optimizes operation efficiency and reduces implementation costs.

Abstract: A semiconductor device and a manufacturing method of the semiconductor device. The semiconductor device includes: a semiconductor substrate; a bottom electrode metal layer and a top electrode metal layer located on the semiconductor substrate; a resistive layer located between the bottom electrode metal layer and the top electrode metal layer, where the transverse width of the resistive layer is greater than the transverse width of the bottom electrode metal layer and/or the top electrode metal layer, and the resistive layer has a variable resistance; an oxygen barrier layer located between the bottom electrode metal layer and the top electrode metal layer, where the oxygen barrier layer is located above the resistive layer; and an oxygen grasping layer located between the bottom electrode metal layer and the top electrode metal layer, where the transverse width of the oxygen grasping layer is less than the transverse width of the resistive layer.

Abstract: An in-memory computing processor, an in-memory computing processing system, an in-memory computing processing apparatus, and a deployment method of an algorithm model based on the in-memory computing processor are disclosed. The in-memory computing processor includes a first master control unit and a plurality of memristor processing modules, and the first master control unit is configured to be capable of dispatching and controlling the plurality of memristor processing modules, the plurality of memristor processing modules are configured to be capable of calculating under the dispatch and control of the first master control unit, and the plurality of memristor processing modules are further configured to be capable of communicating independently of the first master control unit to calculate.

Abstract: A nanowire transistor and a manufacture method thereof are provided. The nanowire transistor includes a semiconductor wire, a semiconductor layer, a source electrode and a drain electrode. The semiconductor wire includes a first semiconductor material and includes a source region, a drain region, and a channel region, along an axial direction of the semiconductor wire, the channel region is between the source region and the drain region; the semiconductor layer includes a second semiconductor material and covers the channel region of the semiconductor wire; the source electrode is in the source region of the semiconductor wire and is in direct contact with the source region of the semiconductor wire, and the drain electrode is in the drain region of the semiconductor wire and is in direct contact with the drain region of the semiconductor wire.

Abstract: A generative adversarial network device and a training method thereof. The generative adversarial network device includes a generator and a discriminator. The generator is configured to generate a first sample according to an input data; the discriminator is coupled to the generator, and is configured to receive the first sample and be trained based on the first sample; the generator includes a first memristor array serving as a first weight array. The generative adversarial network device can omit a process of adding noise to fake samples generated by the generator, thereby saving training time, reducing resource consumption and improving training speed of the generative adversarial network.

Abstract: A method for generating a random number and a random number generator are provided. The method for generating a random number includes: performing n writing operations on at least one analog resistive random access memory, where each of the n writing operations includes applying at least one writing operation pulse to change a conductance value of an operated analog resistive access memory; and generating the random number based on n writing operation pulse numbers respectively corresponding to the n writing operations, where n is a positive integer. The method for generating a random number generates random numbers based on the analog characteristics of the analog resistive random access memory, the generated random number does not need back-end correction, and have both high speed and high reliability.

Abstract: A training method and a training device for a neural network based on memristors are provided. The neural network includes a plurality of neuron layers connected one by one and weight parameters between the plurality of neuron layers, and the training method includes: training the weight parameters of the neural network, and programming a memristor array based on the weight parameters after being trained to write the weight parameters after being trained into the memristor array; and updating a critical layer or several critical layers of the weight parameters of the neural network by adjusting conductance values of at least part of memristors of the memristor array.

Abstract: Disclosed are a parallel acceleration method for a memristor-based neural network, a parallel acceleration processor based on a memristor-based neural network and a parallel acceleration device based on a memristor-based neural network. The neural network includes a plurality of functional layers sequentially provided, wherein the plurality of functional layers include a first functional layer and a second functional layer following the first functional layer, the first functional layer includes a plurality of first memristor arrays in parallel, and the plurality of first memristor arrays are configured to execute an operation of the first functional layer in parallel and to output a result of the operation to the second functional layer. The parallel acceleration method includes: executing the operation of the first functional layer in parallel via the plurality of first memristor arrays and outputting the result of the operation to the second functional layer.

Abstract: A circuit structure and a driving method thereof, a neural network are disclosed. The circuit structure includes at least one circuit unit, each circuit unit includes a first group of resistive switching devices and a second group of resistive switching devices, the first group of resistive switching devices includes a resistance gradual-change device, the second group of resistive switching devices includes a resistance abrupt-change device, the first group of resistive switching devices and the second group of resistive switching devices are connected in series, in a case that no voltage is applied, a resistance value of the first group of resistive switching devices is larger than a resistance value of the second group of resistive switching devices.

Abstract: A fermented beverage and a preparation method are disclosed. The method uses ripened fruits and/or vegetables as raw materials. The method includes performing a natural ripening on a fruit and/or a vegetable at 15° C. to 30° C. for 5 days to 10 days to obtain a ripened fruit and/or vegetable; and inoculating lactic acid bacteria into the ripened fruit and/or vegetable, and performing a fermentation for 10 days to 30 days at 15° C. to 30° C. to obtain the fermented beverage. After 23 days of fermentation, the fermented beverage has a total flavonoid content up to 0.5 mg/mL to 2.0 mg/mL, which is beneficial to human health, a pH of 3.2 to 3.8 and a total titratable acidity of up to 12 mg/mL to 18 mg/mL. The fermented beverage requires a short fermentation cycle, which is beneficial to industrial production.

Abstract: Embodiments of this application provide a method for preparing a thin film piezoresistive material, a thin film piezoresistive material, a robot, and a device. The method includes: determining a mass ratio of conductive particles to a cross-linked polymer in preparation of the thin film piezoresistive material, a value range of the mass ratio being 3:97 to 20:80; dispersing the conductive particles and the cross-linked polymer in a solvent according to the mass ratio, to obtain a first dispersion; and curing the first dispersion by using a liquid dropping method within a temperature range of 25° C. to 200° C., to obtain the thin film piezoresistive material. The technical solutions provided by the embodiments of this application provide a method for preparing a thin film piezoresistive material through liquid dropping, thereby effectively controlling the thickness of the piezoresistive material, so that the prepared thin film piezoresistive material has a relatively small thickness.

Abstract: This application discloses a conductive paste, a preparation method thereof, and a preparation method of a conductive film using the conductive paste. The conductive paste comprises: a thermoplastic polyurethane, conductive particles, and an organic solvent, the thermoplastic polyurethane and the conductive particles being proportionally mixed in the organic solvent, and the thermoplastic polyurethane being dispersed in the form of particles among the conductive particles. A thermoplastic polyurethane elastomer is used as a binder, and the conductive particles are mixed in the organic solvent containing the thermoplastic polyurethane elastomer. The conductive particles ensure the conductivity of the conductive film prepared using the conductive paste. The thermoplastic polyurethane has strong adhesion ability, and is suitable for use on the surface of most substrates, to form a conductive film with good adhesion and no cracking.

Abstract: A neuron simulation circuit and a neural network apparatus. The neuron simulation circuit includes an operational amplifier, a first resistive device and a second resistive device. The operational amplifier includes a first input terminal, a second input terminal, and an output terminal. The first resistive device is connected between the first input terminal or the second input terminal of the operational amplifier and the output terminal of the operational amplifier. The second resistive device is connected between the output terminal of the operational amplifier and an output terminal of the neuron simulation circuit. The second resistive device includes a threshold switching memristor, and a first terminal of the threshold switching memristor is electrically connected with the output terminal of the neuron simulation circuit. At least one of the first resistive device and the second resistive device includes a dynamic memristor.

Abstract: A resistive random access memory and a manufacture method thereof are provided. The resistive random access memory includes: a first electrode, a second electrode, a resistive layer between the first electrode and the second electrode, and at least one thermal enhanced layer; the thermal enhanced layer is adjacent to the resistive layer, and a thermal conductivity of the thermal enhanced layer is less than a thermal conductivity of the first electrode and a thermal conductivity of the second electrode.

Abstract: A compilation method, a compilation apparatus suitable for an In-Memory Computing apparatus, a computing device and a storage medium. The compilation method includes: acquiring calculation information of an algorithm to be compiled; converting the algorithm to be compiled into the first intermediate representation according to the calculation information; mapping the first intermediate representation to the second intermediate representation; and compiling the algorithm to be compiled into instruction information recognized by the In-Memory Computing apparatus according to the hardware information, to make the In-Memory Computing apparatus execute the instruction information. The compilation method may compile the calculation information into instructions that may be directly executed by the In-Memory Computing apparatus, so as to realize the effect of accelerating the operations of various algorithms by using the In-Memory Computing apparatus.

Abstract: A memristor and a preparation method thereof are provided. The memristor includes at least one memristive unit, each of the at least one memristive unit includes a transistor and at least one memristive component, the transistor includes a source electrode and a drain electrode; and each of the at least one memristive component includes a first electrode, a resistive layer, a second electrode, and a passivation layer, the first electrode is electrically connected with the source electrode or the drain electrode; the resistive layer is provided between the first electrode and the second electrode; and the passivation layer at least covers a sidewall of the resistive layer.

Abstract: A signal processing apparatus and a signal processing method are provided. The signal processing apparatus includes a memristor array, an input circuit, a first switching circuit, a second switching circuit, an output circuit, and a control circuit. The memristor array includes memristor units and is connected to source lines, word lines and bit lines. The control circuit is configured to control the first switching circuit to select at least one source line to apply at least one first signal to the at least one source line respectively, control the second switching circuit to select and activate at least one word line to apply the at least one first signal to a memristor unit corresponding to the at least one word line, and control the output circuit to output a plurality of second signals based on conductivity values of memristors of the memristor array.