Abstract: A system performs distributed or parallel pattern extraction and clustering for pattern classification of large layouts of electronic circuits. The system identifies circuit patterns with a layout representation. The system encodes the circuit patterns using a neural network based autoencoder to generate encoded circuit patterns that can be stored efficiently. The system clusters the encoded circuit patterns into an arbitrary number of clusters based upon a high degree of similarity. The clusters of circuit patterns may be used for training and evaluation of machine learning based models.

Abstract: A method for tuning an analog front end response is provided. The method includes determining a peaking control value for an analog front end (AFE) of a receiver, determining an attribute corresponding to the peaking control value, selecting the peaking control value as the operating peaking control value for the AFE based on the attribute being determined to be higher than a previous attribute, and performing a receiver adaptation using the peaking control for a one or more transmitter configurations.

Abstract: An integrated circuit (IC) device and a method for communicating test data utilizes test control circuitry, and a test controller. The test controller is coupled with the test control circuitry and decodes packetized test pattern data to identify configuration data for the test controller and test data for the test control circuitry. The test controller further communicates the test data to the test control circuitry, and packetizes resulting data received from the test control circuitry. The resulting data corresponds to errors identified by a test performed based on the test pattern data.

Abstract: An equivalent input characterization waveform (EICW) is determined for a channel-connected block (CCB) located on a boundary of a cell, for a specific waveform of interest. The EICW and the specific waveform of interest produce a same timing characteristic of the CCB, but the EICW belongs to a set of waveforms on which a behavioral timing model for the multi-stage cell is based whereas the specific waveform of interest is not so limited. A timing response of the multi-stage cell is then estimated based on applying the EICW.

Abstract: A method comprises receiving an integrated circuit (IC) chip design, and generating, by one or more processors, a wafer image and a wafer target from the IC chip design. The method further comprises generating, by the one or more processors, sensitivity information based on a determination that the wafer image and the wafer target converge, and outputting the sensitivity information. The sensitivity information is associated with writing a mask written for the IC chip design.

Abstract: A multi-port, multi-mode Reed Solomon (RS) forward error correction system includes a plurality of data in lines, each associated with a data port. The system includes a syndrome block (SDM) that has a plurality of syndrome slices and a SDM switching logic. An input of a SDM slice couples with a data in line from the plurality of data in lines. The switching logic couples with an interface port width (IFW) line a mode line. The IFW line identifies a number of data in lines tied together and the mode line to identify a RS mode. A reformulated inversionless Berlekamp-Massey (RiBM) block has a plurality of RiBM slices and a RiBM switching logic. A Chien Forney (ChFr) block has a plurality of ChFr slices. An error evaluation magnitude (ErEval) block has a plurality of ErEval slices. A plurality of adders couple with an output of a corresponding ErEval slice.

Abstract: A method includes receiving a design file for a circuit design and receiving a library that defines a cell that includes one or more inputs, a first combinational logic circuit element, a second combinational logic circuit element, a first output, and a second output. The method also includes replacing a plurality of circuit elements in the circuit design with the cell and compiling the circuit design after replacing the plurality of circuit elements with the cell. The first and second outputs of the cell in the compiled circuit design replace a plurality of outputs of the plurality of circuit elements.

Abstract: A method for forming ultra-high density integrated circuitry, such as for a 6T SRAM, for example, is provided. The method involves applying double patterning litho-etch litho-etch (LELE) and using a spacer process to shrink the critical dimension of features. To improve process margins, the method implements a double-patterning technique by modifying the layout and splitting cross-coupling straps into two colors (e.g., each color corresponds to a mask-etch process). In addition, a spacer process is implemented to shrink feature size and increase the metal-to-metal spacing between the two cross-coupling straps, in order to improve process margin and electrical performance. This is achieved by depositing a spacer layer over an opening in a hardmask, followed by spacer etch back. The opening is thus shrunk by the amount of spacer thickness. The strap-to-strap spacing may then be increased by twice the amount of spacer thickness.

Abstract: A circuit includes, in part, a first transistor receiving a first clock signal at its gate, a second transistor receiving a second clock signal at its gate, a first impedance coupled to the drain terminal of the first transistor, a second impedance coupled to the drain terminal of the second transistor, a current source coupled to the source terminals of the first and second transistors, a third transistor receiving a third clock signal at its gate, a fourth transistor receiving a fourth clock signal at its gate, a fifth transistor coupling the drain terminal of the third transistor to the second impedance in response to a first control signal, a sixth transistor coupling the drain terminal of the fourth transistor to the second impedance in response to a second control signal, and a first variable current source coupled to the source terminals of the third and fourth transistors.

Abstract: Glitch source identification and ranking is provided by: identifying a plurality of glitch sources in a circuit layout; back referencing the plurality of glitch sources to corresponding lines in a Resistor Transistor Logic (RTL) file defining the plurality of glitch sources; identifying, in the circuit layout, a plurality of glitch terminuses associated with the plurality of glitch sources; determining a plurality of glitch power consumption values associated with the plurality of glitch sources based on fanouts in the circuit layout extending from the plurality of glitch sources to the plurality of glitch terminuses; ranking, by a processor, the plurality of glitch sources based on corresponding glitch power consumption values of the plurality of glitch power consumption values corresponding to individual glitch sources of the plurality of glitch sources; and reporting the corresponding lines in the RTL file associated with the ranked plurality of glitch sources.

Abstract: A method includes generating a plurality of vector sequences based on input signals of an electric circuit design and encoding the plurality of vector sequences. The method also includes clustering the plurality of encoded vector sequences into a plurality of clusters and selecting a set of encoded vector sequences from the plurality of clusters. The method further includes selecting a first set of vector sequences corresponding to the selected set of encoded vector sequences, selecting a second set of vector sequences from the plurality of vector sequences not in the first set of encoded vector sequences, and training, by a processing device, a machine learning model to predict power consumption using the first and second sets of vector sequences.

Abstract: A method comprises receiving an integrated circuit (IC) chip design, and generating, by one or more processors and based on the IC chip design, dose information, a wafer image, and a wafer target. Further, the method comprises modifying, by the one or more processors, the dose information based on a comparison of the wafer image and the wafer target. Further, the method comprises outputting the modified dose information to a mask writing device.

Abstract: A method for error correction comprises receiving data at a first device, and decoding, by decoder circuitry of the first device, the data. Decoding the data comprises determining a first error location within the data, and determining a first error magnitude within the data in parallel with determining the first error location. Decoding the data further comprises performing error correction to generate the decoded data based on the first error location and the first error magnitude. The method further comprises transmitting the decoded data to a second device.

Abstract: A first set of critical dimension (CD) measurements of resist patterns created by a lithography process and a second set of CD measurements of water patterns created by an etch process may be obtained. A forward etch model and an inverse etch model may be calibrated together by reducing (1) a first prediction error between the second set of CD measurements and a first set of simulated CDs predicted by the forward etch model based on the resist patterns, a second prediction error between the first set of CD measurements and a second set of simulated CDs predicted by the inverse etch model based on the wafer patterns, and a matching error between the forward etch model and the inverse etch model.

Abstract: Aspects described herein relate to obtaining a mask pattern using a cost function gradient (CFG) generated from a Jacobian matrix generated from a perturbation look-up table (PLT). In an example method, a PLT is populated (108). Each table entry of the PLT is based on a respective perturbed intensity signal. The respective perturbed intensity signal is based on a simulated signal received at an image surface using a mask pattern having a perturbed element of the mask pattern. The mask pattern is for a design of an integrated circuit. A matrix is populated (110) using the PLT and a target intensity signal. The target intensity signal is based on a signal received at the image surface to form target features at the image surface. A CFG is defined (112) based on the matrix. An analysis is performed (114) on the mask pattern based on the CFG.

Abstract: A method of generating a mask used in fabrication of a semiconductor device includes, in part, selecting a source candidate, generating a process simulation model that includes a defect rate in response to the selected source candidate, performing a first optical proximity correction (OPC) on the data associated with the mask in response to the process simulation model, assessing one or more lithographic evaluation metrics in response to the OPC mask data, computing a cost in response to the assessed one or more lithographic evaluation metrics, and determining whether the computed cost satisfies a threshold condition. In response to the determination that the computed cost does not satisfy the threshold condition, a different source candidate may be selected.

Abstract: Systems and methods for asynchronous communication are disclosed. For example, a method for asynchronous communication includes encoding, by a transmitter circuit and according to a first clock signal, a bit sequence by converting a one-bit in the bit sequence into a first sequence and a zero-bit in the bit sequence into a second sequence. A length of the first sequence and a length of the second sequence differ by at least three bits. The method also includes communicating, by the transmitter circuit, the first sequence and the second sequence to a receiver circuit that decodes the first sequence and the second sequence according to a second clock signal that is independent of the first clock signal.

Abstract: A circuit can include a first sub-circuit, a second sub-circuit, and a third sub-circuit. The first sub-circuit can store a reset state or a set state, and can include a first Josephson junction (JJ), a second JJ, and a third JJ coupled in parallel using superconducting inductors. The first JJ, the second JJ, and the third JJ can be biased using a JJ-based current source. The second sub-circuit can switch the first sub-circuit to the set state in response to receiving a pulse. The third sub-circuit can switch the first sub-circuit to the reset state in response to receiving one or more pulses.

Abstract: Some aspects of this disclosure are directed automated performance tuning of a hardware description language (HDL) simulation system. For example, some aspects of this disclosure relate to a method, including generating, by a first subsystem optimizer, a plurality of recommendations corresponding to a first subsystem of a hardware description language (HDL) simulation system. The plurality of recommendations are generated by the first subsystem optimizer using one or more optimization applications. The method further includes generating, by the first subsystem optimizer, a first aggregate recommendation by combining the plurality of recommendations corresponding to the first subsystem of the HDL simulation system. The method further includes updating a configuration of the first subsystem of the HDL simulation system based on the first aggregate recommendation, wherein the HDL simulation system is configured to simulate a circuit design using the updated configuration during execution of the first subsystem.

Abstract: A machine learning (ML) system is trained to predict the number of design rules violations of a circuit design that includes a multitude of Gcells. To achieve this, a netlist associated with the circuit design is placed by a place and route tool. A first list of features associated with the placed netlist is delivered to the ML system. A global route of the circuit design is performed by a global router. Next, a second list of features is delivered from the global router to the ML system. Thereafter, a detailed route of the circuit design is performed by a detailed router. A label associated with each Gcell in the circuit design is delivered to the ML system from the detailed route. The ML system is trained using the first and second list of features and the labels.