A signal control device includes a charge/discharge circuit, a sampling capacitor, and an AC conversion circuit. The charge/discharge circuit is capable of charging or discharging the sampling capacitor. The AC conversion circuit performs an AD conversion by converting an analog voltage value charged in the sampling capacitor into an AD conversion value that is a digital value. After a charge operation or a discharge operation to the sampling capacitor with the charge/discharge circuit, the AD conversion circuit performs the AD conversion, and a malfunction of the charge/discharge circuit is determined based on a diagnosis result of the AD conversion value.

A physical quantity detection circuit includes: a detection signal generation circuit generating a detection signal, based on an output signal from a physical quantity detection element; an analog/digital converter circuit converting the detection signal into a first digital signal and converting a test signal into a second digital signal; a test signal generation circuit generating the test signal; and a malfunction diagnosis circuit diagnosing a malfunction of the analog/digital converter circuit, based on the second digital signal. A full-scale voltage of the analog/digital converter circuit is selected from among a plurality of voltages having different magnitudes, according to a power supply voltage. The test signal includes an upper limit value test signal, a lower limit value test signal, and a first intermediate value test signal. The test signal generation circuit performs resistive voltage division of the full-scale voltage and thus generates the first intermediate value test signal.

An integrated circuit for controlling a sensor chip capable of sensing various materials includes a plurality of amplifier clusters, a plurality of analog multiplexers, and at least one analog-to-digital converter coupled the analog multiplexers and configured to generate digital code values representative of electrical signals. Each of the amplifier clusters include four amplifiers, each amplifier has a first input coupled to a sensor of the sensor chip, and a second input coupled to a programmable voltage reference. Each one of the analog multiplexers is coupled to one of the amplifier clusters and configured to selectively pass through an electrical signal to the at least one analog-to-digital converter.

A reference voltage generator comprises a comparator, a digital-to-analog converter (DAC) and a switched capacitor accumulator. The comparator receives a reference voltage input, a feedback input, and a control signal. The DAC is coupled to an output of the comparator, and the switched capacitor accumulator is coupled to an output of the DAC. In some implementations, a digital filter is coupled between the output of the comparator and the input of the DAC. The switched capacitor accumulator can be coupled to a buffer that outputs the feedback input and a reference voltage for an analog-to-digital converter (ADC). In some implementations, the feedback loop includes N one-bit DACs coupled to the output of the comparator and N switched capacitor accumulators, each of which is coupled to a unique one-bit DAC.

Methods of performing mixed-signal/analog multiply-accumulate (MAC) operations used for matrix multiplication in fully connected artificial neural networks in integrated circuits (IC) are described in this disclosure having traits such as: (1) inherently fast and efficient for approximate computing due to current-mode signal processing where summation is performed by simply coupling wires, (2) free from noisy and power hungry clocks with asynchronous fully-connected operations, (3) saving on silicon area and power consumption for requiring neither any data-converters nor any memory for intermediate activation signals, (4) reduced dynamic power consumption due to Compute-In-Memory operations, (5) avoiding over-flow conditions along key signals paths and lowering power consumption by training MACs in neural networks in such a manner that the population and or combinations of multi-quadrant activation signals and multi-quadrant weight signals follow a programmable statistical distribution profile, (6) programmable current consumption versus degree of precision/approximate computing, (7) suitable for ‘always-on’ operations and capable of ‘self power-off’, (8) inherently simple arrangement for non-linear activation operations such as Rectified Linear Unit, ReLu, and (9) manufacturable on main-stream, low cost, and lagging edge standard digital CMOS process requiring neither any resistors nor any capacitors.

A circuit includes an analog-to-digital converter (ADC) having selectable first and second analog channel inputs; a window comparator that compares a digital value output by the ADC to first and second threshold values defining a window and that asserts a trigger signal in response to the digital value being outside the window; a programmable clock circuit that provides a clock signal to the ADC; a controller that generates, in response to assertion of the trigger signal, a sample rate control signal to cause the clock circuit to increase the frequency of the clock signal and toggle selection between the first and second analog channel inputs; and comparison circuitry that compares a first digital output from the ADC to a second digital output from the ADC.

An analog-to-digital converter (ADC) includes a coarse ADC that receives an analog input voltage, generates a first digital signal based on the analog input voltage using a successive approximation register (SAR) method, and outputs a residual voltage remaining after the first digital signal is generated. The ADC further includes an amplifier that receives the residual voltage and a test voltage, generates a residual current by amplifying the residual voltage by a predetermined gain, and generates a test current by amplifying the test voltage by the gain. The ADC further includes a fine ADC that receives the residual current and generates a second digital signal based on the residual current using the SAR method, and an auxiliary path that receives the test current and generates a gain correction signal based on the test current. The gain of the amplifier is adjusted based on the gain correction signal.

An integrated circuit includes an analog-to-digital converter (ADC) having selectable first and second analog channel inputs and a digital output. A window comparator coupled to the digital output. The window comparator configured to compare a digital value on the digital output to first and second threshold values. A programmable clock circuit configured to provide a clock signal to the ADC. A controller that, response to assertion of the trigger signal, is configured to generate a sample rate control signal to the clock circuit to cause the clock circuit to increase the frequency of the clock signal and toggle selection between the first and second analog channel inputs. A result comparison circuit having a comparison input coupled to the digital output. The result comparison circuit is configured to compare a first digital conversion output from the ADC to a second digital conversion output from the ADC.

A successive approximation analog-to-digital converter includes a digital-to-analog converter DAC configured to receive a digital signal. First conversion units of the DAC are configured to sample an analog signal via a first switch and provide a first level voltage. Each first conversion unit includes a first capacitor array and a first switch array controlled from the digital signal. A single second conversion unit of the DAC is configured to provide a second level voltage. The second conversion unit includes a second capacitor array and a second switch array. A comparator operates to compare each of the first level voltages to the second level voltage and to provide a comparison signal based on each comparison and actuation of a set of third switches. A control circuit closes the first switches simultaneously and closes the third switches successively for the conversion of each sampled analog signal.

A machine learning (ML)-based phase current balancer for a multiphase power converter includes one or more inputs, one or more outputs, and an artificial neural network. The artificial neural network includes a plurality of artificial neurons and is trained to provide corrective phase current imbalance information at the one or more outputs for correcting phase current imbalance within the multiphase power converter, based on information available at the one or more inputs and indicative of individual phase currents of the multiphase power converter.