Abstract: Embodiments of the present invention provide a two-phase process for searching the root causes of the yield loss in the production line 100. In a first phase, process tools and their process tool types that are likely to cause the yield loss are identified, and in a second phase, the process parameters that are likely to cause the yield loss within the process tool types found in the first phase are identified. In each phase, two different algorithms can be used to generate a reliance index (RIk) for gauge the reliance levels of their search results.

Abstract: In a metrology sampling method with a sampling rate decision scheme, a mean absolute percentage error (MAPE) and a maximum absolute percentage error (MaxErr) of visual metrology values of all workpieces in a set of determinative samples (DS), and various index values that can detect various status changes of a process tool (such as maintenance operation, parts changing, parameter adjustment, etc.), and/or information abnormalities of the process tool (such as abnormal process data, parameter drift/shift, abnormal metrology data, etc.) appearing in a manufacturing process are applied to develop an automated sampling decision (ASD) scheme for reducing a workpiece sampling rate while VM accuracy is still sustained.

Abstract: A baseline predictive maintenance method for a target device (TD) and a computer program product thereof are provided. Fresh samples which are generated when the target device produces workpieces just after maintenance are collected, and a new workpiece sample which is generated when the target device produces a new workpiece is collected. A plurality of modeling samples are used to build a TD baseline model in accordance with a conjecturing algorithm, wherein the modeling samples include the new workpiece sample and the fresh samples. A TD healthy baseline value for the new workpiece is computed by the TD baseline model, and a device health index (DHI), a baseline error index (BEI) and baseline individual similarity indices (ISIB) are computed, thereby achieving the goals of fault detection and classification (FDC) and predictive maintenance (PdM).

Abstract: Embodiments of the present invention provide a two-phase process for searching the root causes of the yield loss in the production line 100. In a first phase, process tools and their process tool types that are likely to cause the yield loss are identified, and in a second phase, the process parameters that are likely to cause the yield loss within the process tool types found in the first phase are identified. In each phase, two different algorithms can be used to generate a reliance index (RIk) for gauge the reliance levels of their search results.

Abstract: In a metrology sampling method with a sampling rate decision scheme, a mean absolute percentage error (MAPE) and a maximum absolute percentage error (MaxErr) of visual metrology values of all workpieces in a set of determinative samples (DS), and various index values that can detect various status changes of a process tool (such as maintenance operation, parts changing, parameter adjustment, etc.), and/or information abnormalities of the process tool (such as abnormal process data, parameter drift/shift, abnormal metrology data, etc.) appearing in a manufacturing process are applied to develop an automated sampling decision (ASD) scheme for reducing a workpiece sampling rate while VM accuracy is still sustained.

Abstract: The present invention discloses a brain volumetric measuring method for measuring brain volumetric changes of a subject. The method at least comprises the following steps. First, a light source is provide and emitted into the head of the subject through a light source emitting position. And then, a first optical signal is obtained by receiving numerous scattered photons from the head of the patient through several second positions of. A second optical signal will be obtained by processing the first optical signal. The present invention also discloses a brain volumetric measuring system for performing the abovementioned method.

Abstract: A baseline predictive maintenance method for a target device (TD) and a computer program product thereof are provided. Fresh samples which are generated when the target device produces workpieces just after maintenance are collected, and a new workpiece sample which is generated when the target device produces a new workpiece is collected. A plurality of modeling samples are used to build a TD baseline model in accordance with a conjecturing algorithm, wherein the modeling samples include the new workpiece sample and the fresh samples. A TD healthy baseline value for the new workpiece is computed by the TD baseline model, and a device health index (DHI), a baseline error index (BEI) and baseline individual similarity indices (ISIB) are computed, thereby achieving the goals of fault detection and classification (FDC) and predictive maintenance (PdM).