Abstract: A carrier structure is provided, in which at least one positioning area is defined on a chip-placement area of a package substrate, and at least one alignment portion is disposed on the positioning area. Therefore, the precision of manufacturing the alignment portion is improved by disposing the positioning area on the chip-placement area, such that the carrier structure can provide a better alignment mechanism for the chip placement operation.

Abstract: A method includes receiving first data associated with an equipment parameter. The first data is indicative of an equipment setting of a process tool of a plurality of process tools at a first manufacturing system. The method further includes providing the first data as input to a trained machine learning model. The trained machine learning model is trained using historical data pertaining to equipment parameters of the plurality of process tools at the first manufacturing system. The method further includes obtaining, as output of the trained machine learning model, a predicted value of a metric corresponding to the equipment parameter. The method further includes comparing the predicted value of the metric with the first data, and performing a corrective action based on the comparing.

Abstract: The invention discloses a stereo enhancement system and a stereo enhancement method. The stereo enhancement system includes a beamforming unit and a signal processing unit. The beamforming unit is used for receiving a plurality of input sound signals and generating a plurality of beamforming sound signals corresponding to a plurality of direction intervals respectively. The signal processing unit is coupled to the beamforming unit and used for receiving the plurality of beamforming sound signals corresponding to the plurality of direction intervals respectively and generating a first synthesized output sound signal and a second synthesized sound signal accordingly.

Abstract: A fan control system applied to N fans inside a computer system is disclosed, comprising: a main microphone, a control circuitry, a wave generation circuitry and a number N of fan controllers. The control circuitry calculates a basic frequency value according to a temperature inside the computer system, and continuously updates a parameter by any known optimization algorithm according to a main audio signal from the main microphone. The wave generation circuitry generates N square waves according to the basic frequency value and the parameter. The N fan controllers respectively form and transmit N modulation signals to the N fans according to the N square waves and N tachometric signals from the N fans. The parameter is one of a frequency variation and a set of phase differences, and the N square waves have the same frequency.

Abstract: A microphone system is disclosed, comprising: a microphone array and a processing unit. The microphone array comprises Q microphones that detect sound and generate Q audio signals. The processing unit is configured to perform operations comprising: spatial filtering over the Q audio signals using a trained model based on at least one target beam area (TBA) and coordinates of the Q microphones to generate a beamformed output signal originated from ? target sound source inside the at least one TBA, where ?>=0. Each TBA is defined by r time delay ranges for r combinations of two microphones out of the Q microphones, where Q>=3 and r>=1. A dimension of a first number for locations of all sound sources able to be distinguished by the processing unit increases as a dimension of a second number for a geometry formed by the Q microphones increases.

Abstract: A microphone system of the invention is applicable to an electronic device comprising an adjustable mechanism that causes a change in geometry of a microphone array. The microphone system comprises the microphone array, a sensor and a beamformer. The microphone array comprises multiple microphones that detect sound and generate multiple audio signals. The sensor detects a mechanism variation of the electronic device to generate a sensing output. The beamformer is configured to perform a set of operations comprising: performing a spatial filtering operation over the multiple audio signals using a trained model based on the sensing output, one or more first sound sources in one or more desired directions and one or more second sound sources in one or more undesired directions to generate a beamformed output signal originated from the one or more first sound sources.

Abstract: A hearing device is disclosed, comprising a main microphone, M auxiliary microphones, a transform circuit, a processor, a memory and a post-processing circuit. The transform circuit transforms first sample values in current frames of a main audio signal and M auxiliary audio signals from the microphones into a main and M auxiliary spectral representations. The memory includes instructions to be executed by the processor to perform operations comprising: performing ANC over the first sample values using an end-to-end neural network to generate second sample values; and, performing audio signal processing over the main and the M auxiliary spectral representations using the end-to-end neural network to generate a compensation mask. The post-processing circuit modifies the main spectral representation with the compensation mask to generate a compensated spectral representation, and generates an output audio signal according to the second sample values and the compensated spectral representation.

Abstract: An own voice suppression apparatus applicable to a hearing aid is disclosed. The own voice suppression apparatus comprises: an air conduction sensor, an own voice indication module and a suppression module. The air conduction sensor is configured to generate an audio signal. The own voice indication module is configured to generate an indication signal according to at least one of user's mouth vibration information and user's voice feature vector comparison result. The suppression module coupled to the air conduction sensor and the own voice indication module is configured to generate an own-voice-suppressed signal according to the indication signal and the audio signal.

Abstract: A voice event detection apparatus is disclosed. The apparatus comprises a vibration to digital converter and a computing unit. The vibration to digital converter is configured to convert an input audio signal into vibration data. The computing unit is configured to trigger a downstream module according to a sum of vibration counts of the vibration data for a number X of frames. In an embodiment, the voice event detection apparatus is capable of correctly distinguishing a wake phoneme from the input vibration data so as to trigger a downstream module of a computing system. Thus, the power consumption of the computing system is saved.

Abstract: A fan control system applied to N fans inside a computer system is disclosed, comprising: a main microphone, a control circuitry, a wave generation circuitry and a number N of fan controllers. The control circuitry calculates a basic frequency value according to a temperature inside the computer system, and continuously updates a parameter by any known optimization algorithm according to a main audio signal from the main microphone. The wave generation circuitry generates N square waves according to the basic frequency value and the parameter. The N fan controllers respectively form and transmit N modulation signals to the N fans according to the N square waves and N tachometric signals from the N fans. The parameter is one of a frequency variation and a set of phase differences, and the N square waves have the same frequency.

Abstract: There is provided a smart engine including a profile collector and a main processing module. The profile collector is configured to store a plurality of profiles, one or more suitable profiles being dynamically selected according to an instruction from a user or an automatic selector. The main processing module is connected to the profile collector and directly or indirectly connected to a sensor, and configured to perform a detailed analysis to determine detailed properties of features, objects, or scenes based on suitable sensor data from the sensor.

Abstract: A speech processing apparatus applied in a communication device having a mechanical defect is disclosed. The apparatus comprises an acoustic echo cancellation (AEC) unit, a multiplier and a processor. The AEC unit cancels an echo in a first audio signal from a microphone using a known AEC algorithm to generate a second audio signal. The multiplier multiplies corresponding M frames of a downlink audio signal by a gain to provide a gained downlink signal for a speaker. The processor performs operations comprising: muting an uplink audio signal when a first power level for M frames of a first input signal is less than a first threshold value; and, reducing the gain when the first power level and a second power level for M frames of a second input signal are respectively greater than the first threshold value and a second threshold value.

Abstract: A hearing device is disclosed, comprising a main microphone, M auxiliary microphones, a transform circuit, a processor, a memory and a post-processing circuit. The transform circuit transforms first sample values in current frames of a main audio signal and M auxiliary audio signals from the microphones into a main and M auxiliary spectral representations. The memory includes instructions to be executed by the processor to perform operations comprising: performing ANC over the first sample values using an end-to-end neural network to generate second sample values; and, performing audio signal processing over the main and the M auxiliary spectral representations using the end-to-end neural network to generate a compensation mask. The post-processing circuit modifies the main spectral representation with the compensation mask to generate a compensated spectral representation, and generates an output audio signal according to the second sample values and the compensated spectral representation.

Abstract: An in-memory computing memory device is disclosed. The memory device comprises an array of memory cells, a plurality of word lines, a plurality of bit lines, (M+1) input circuits, a wordline driver and an evaluation circuitry. The array is divided into (M+1) lanes and each lane comprises P memory cell columns and an input circuit. The input circuit in each lane charges a predefined bit line with a default amount of charge proportional to an input synapse value and then distributes the default amount of charge to the other second bit lines with a predefined ratio based on a constant current. The evaluation circuitry couples a selected number of the bit lines to an accumulate line and convert an average voltage at the accumulate line into a digital value in response to a set of (M+1) input synapse values and the activated word line.

Abstract: A speech enhancement apparatus is disclosed and comprises an adaptive noise cancellation circuit, a blending circuit, a noise suppressor and a control module. The ANC circuit filters a reference signal to generate a noise estimate and subtracts a noise estimate from a primary signal to generate a signal estimate based on a control signal. The blending circuit blends the primary signal and the signal estimate to produce a blended signal. The noise suppressor suppresses noise over the blended signal using a first trained model to generate an enhanced signal and a main spectral representation from a main microphone and M auxiliary spectral representations from M auxiliary microphones using (M+1) second trained models to generate a main score and M auxiliary scores. The ANC circuit, the noise suppressor and the trained models are well combined to maximize the performance of the speech enhancement apparatus.

Abstract: An own voice suppression apparatus applicable to a hearing aid is disclosed. The own voice suppression apparatus comprises: an air conduction sensor, an own voice indication module and a suppression module. The air conduction sensor is configured to generate an audio signal. The own voice indication module is configured to generate an indication signal according to at least one of user's mouth vibration information and user's voice feature vector comparison result. The suppression module coupled to the air conduction sensor and the own voice indication module is configured to generate an own-voice-suppressed signal according to the indication signal and the audio signal.

Abstract: Provided is a speech recognition system including an acoustic model, a decoding graph module, a history buffer, and a decoder. The acoustic model is configured to receive an acoustic input from an input module, divide the acoustic input into audio clips, and return scores evaluated for the audio clips. The decoding graph module is configured to store a decoding graph having at least one possible path of the keyword. The history buffer is configured to store history information corresponding to the possible path in the decoding graph module. The decoder is connected to the acoustic model, the decoding graph module, and the history buffer, and configured to receive the scores from the acoustic model, loop up the possible path in the decoding graph module, and predict an output keyword.

Abstract: A processing apparatus applied in an artificial neuron is disclosed. The processing apparatus comprises a parser, a lookup array, a summing circuit and a MAC circuit. The parser parses one of M packets to extract a non-zero weight value from a header of the one packet, to identify a plurality of bit positions with a specified digit from a payload of the one packet, and to output the non-zero weight value and the bit positions in parallel. The lookup array contains N synapse values and is indexed by the bit positions in parallel to generate a plurality of match values. The summing circuit sums up the match values to generate a sum value. The MAC circuit generates a product of the non-zero weight value and the sum value, and generates an accumulate value based on the product and at least one previous accumulate value.

Abstract: A speech enhancement apparatus is disclosed and comprises an adaptive noise cancellation circuit, a blending circuit, a noise suppressor and a control module. The ANC circuit filters a reference signal to generate a noise estimate and subtracts a noise estimate from a primary signal to generate a signal estimate based on a control signal. The blending circuit blends the primary signal and the signal estimate to produce a blended signal. The noise suppressor suppresses noise over the blended signal using a first trained model to generate an enhanced signal and a main spectral representation from a main microphone and M auxiliary spectral representations from M auxiliary microphones using (M+1) second trained models to generate a main score and M auxiliary scores. The ANC circuit, the noise suppressor and the trained models are well combined to maximize the performance of the speech enhancement apparatus.

Abstract: A voice event detection apparatus is disclosed. The apparatus comprises a vibration to digital converter and a computing unit. The vibration to digital converter is configured to convert an input audio signal into vibration data. The computing unit is configured to trigger a downstream module according to a sum of vibration counts of the vibration data for a number X of frames. In an embodiment, the voice event detection apparatus is capable of correctly distinguishing a wake phoneme from the input vibration data so as to trigger a downstream module of a computing system. Thus, the power consumption of the computing system is saved.