Abstract: A method of making a photomask includes constructing a transmission cross coefficient (TCC) matrix representing an illumination source for supplying light to transmit through the photomask and a pupil for focusing the transmitted light onto a target substrate to produce a set of main features, generating kernels through decomposition of the TCC matrix, selecting ones of the kernels having odd symmetry, generating a field map kernel as a sum of self-convolutions of the odd symmetry kernels, generating a first field map by convolving an area of the photomask corresponding to the set of main features with the field map kernel, and making the photomask corresponding to the first field map. The method may include assigning first sub-resolution assist features (SRAFs) to those portions of the photomask area having corresponding said first field map values exceeding a nonnegative threshold, and making the photomask corresponding to the main features and first SRAFs.

Abstract: A method of making a photomask includes constructing a transmission cross coefficient (TCC) matrix representing an illumination source for supplying light to transmit through the photomask and a pupil for focusing the transmitted light onto a target substrate to produce a set of main features, generating kernels through decomposition of the TCC matrix, selecting ones of the kernels having odd symmetry, generating a field map kernel as a sum of self-convolutions of the odd symmetry kernels, generating a first field map by convolving an area of the photomask corresponding to the set of main features with the field map kernel, and making the photomask corresponding to the first field map. The method may include assigning first sub-resolution assist features (SRAFs) to those portions of the photomask area having corresponding said first field map values exceeding a nonnegative threshold, and making the photomask corresponding to the main features and first SRAFs.

Abstract: Systems and methods are disclosed for a stochastic model of mask process variability of a photolithography process, such as for semiconductor manufacturing. In one embodiment, a stochastic error model may be based on a probability distribution of mask process error. The stochastic error model may generate a plurality of mask layouts having stochastic errors, such as random and non-uniform variations of contacts. In other embodiments, the stochastic model may be applied to critical dimension uniformity (CDU) optimization or design rule (DR) sophistication.

Abstract: Systems and methods are disclosed for a stochastic model of mask process variability of a photolithography process, such as for semiconductor manufacturing. In one embodiment, a stochastic error model may be based on a probability distribution of mask process error. The stochastic error model may generate a plurality of mask layouts having stochastic errors, such as random and non-uniform variations of contacts. In other embodiments, the stochastic model may be applied to critical dimension uniformity (CDU) optimization or design rule (DR) sophistication.

Abstract: Systems and methods are disclosed for a stochastic model of mask process variability of a photolithography process, such as for semiconductor manufacturing. In one embodiment, a stochastic error model may be based on a probability distribution of mask process error. The stochastic error model may generate a plurality of mask layouts having stochastic errors, such as random and non-uniform variations of contacts. In other embodiments, the stochastic model may be applied to critical dimension uniformity (CDU) optimization or design rule (DR) sophistication.

Abstract: Systems and methods are disclosed for a stochastic model of mask process variability of a photolithography process, such as for semiconductor manufacturing. In one embodiment, a stochastic error model may be based on a probability distribution of mask process error. The stochastic error model may generate a plurality of mask layouts having stochastic errors, such as random and non-uniform variations of contacts. In other embodiments, the stochastic model may be applied to critical dimension uniformity (CDU) optimization or design rule (DR) sophistication.