Abstract:
A gas sensing apparatus including a gas sensor, a gas determining circuit and a gas database is provided. The gas sensor includes at least two nanowire sensors. The gas sensor is configured to sense multiple gases and output a plurality of sensing signals. The gas determining circuit is coupled to the gas sensor. The gas determining circuit is configured to receive the sensing signals and determine types of the gases according to reference data and the sensing signals. The gas database is coupled to the gas determining circuit. The gas database stores the reference data and outputs the reference data to the gas determining circuit. Each of the nanowire sensors includes at least one nanowire. Structural properties of the nanowires are different.
Abstract:
A resistive random-access memory device includes a memory array, a read circuit, a write-back logic circuit and a write-back circuit. The read circuit reads the data stored in a selected memory cell and accordingly generates a first control signal. The write-back logic circuit generates a write-back control signal according to the first control signal and a second control signal. The write-back circuit performs a write-back operation on the selected memory cell according to the write-back control signal and a write-back voltage, so as to change a resistance state of the selected memory cell from a low resistance state to a high resistance state, and generates the second control signal according to the resistance state of the selected memory cell.
Abstract:
A varactor is provided. A substrate includes a first surface, a second surface and a first opening and a second opening in the substrate. A conductive material is filling the first and second openings, to form a first through-wafer via (TWV) and a second through-wafer via. A first capacitor is coupled between the first through-wafer via and a first terminal. A second capacitor is coupled between the second through-wafer via and a second terminal. A capacitance of a depletion-region capacitor between the first through-wafer via and the second through-wafer via is determined by a bias voltage applied to the first through-wafer via and the second through-wafer via.
Abstract:
An apparatus and a method for neural network computation are provided. The apparatus for neural network computation includes a first neuron circuit and a second neuron circuit. The first neuron circuit is configured to execute a neural network computation of at least one computing layer with a fixed feature pattern in a neural network algorithm. The second neuron circuit is configured to execute the neural network computation of at least one computing layer with an unfixed feature pattern in the neural network algorithm. The performance of the first neuron circuit is greater than that of the second neuron circuit.
Abstract:
A switching circuit includes a transmission gate, two base control sub-circuits each including a first transistor and a second transistor, a third transistor, and a fourth transistor. The transmission gate includes two I/O terminals, two gate control terminals, and two base control terminals, and is configured to conduct or not conduct the two I/O terminals according to the voltage of the two gate control terminals. The two base voltage control sub-circuits, the third transistor and the fourth transistor forms a double balance circuit structure and is electrically connected to the transmission gate. The double balance circuit changes the voltage of the base control terminals according to the voltage of the I/O terminals of the transmission gate.
Abstract:
A high-frequency component test device including a test key and a test module is provided. The test key includes a front-level key and a back-level key which are arranged symmetrically and have the same electrical length and characteristic impedance. The test module is used to measure an S parameter of the front-level key and the back-level key connected directly and an S parameter of a structure where a device under test (DUT) is added between the front-level key and the back-level key. The test module performs S parameter calculation in the frequency domain and converts the S parameter into an ABCD parameter matrix, and then obtains an ABCD parameter of a de-embedded DUT using a matrix root-opening operation and an inverse matrix operation.
Abstract:
An arrayed switch circuit includes a substrate, signal conductive pads and signal expansion pins. The signal conductive pads are disposed on the substrate at intervals, and the signal conductive pads are arranged to form a signal conductive pad array. Each of the signal conductive pads has a row position and a column position in the signal conductive pad array. A row signal switch is provided between any two adjacent signal conductive pads corresponding to the same row position, and a column signal switch is provided between any two adjacent signal conductive pads corresponding to the same column position. The signal expansion pins are connected to the signal conductive pads located on at least one side of the signal conductive pad array through signal expansion switches respectively.
Abstract:
A readout circuit for a sensor and a readout method thereof are provided. The readout circuit includes a reference circuit, a compensated circuit, and a signal processing circuit. The reference circuit provides a direct current (DC) signal. The compensated circuit is coupled to the reference circuit. The compensated circuit obtains an analog sensing signal of the sensor, obtains the DC signal from the reference circuit, and provides a compensated signal according to the analog sensing signal and the DC signal. The signal processing circuit is coupled to the compensated circuit. The signal processing circuit processes the compensated signal to convert the compensated signal into a digital sensing signal. The compensated circuit subtracts the DC signal from the analog sensing signal to provide the compensated signal.
Abstract:
A neural circuit is provided. The neural circuit includes a neural array. The neural array includes a plurality of semiconductor components. Each of the semiconductor components stores a weighting value to generate a corresponding output current or a corresponding equivalent resistance. The neural array receives a plurality of input signals to control the semiconductor components in the neural array and respectively generates the output currents or changes the equivalent resistances. Since the semiconductor components are coupled to each other, output of the neural array may generate a summation current or a summation equivalent resistance related to the input signals and a weighting condition, so that a computing result exhibits high performance.