Patent ID: 12202015

DETAILED DESCRIPTION

The following disclosure provides many different embodiments, or examples, for implementing different features of the provided subject matter. Specific examples of components and arrangements are described below to simplify the present disclosure. These are, of course, merely examples and are not intended to be limiting. For example, the formation of a first feature over or on a second feature in the description that follows may include embodiments in which the first and second features are formed in direct contact, and may also include embodiments in which additional features may be formed between the first and second features, such that the first and second features may not be in direct contact. In addition, the present disclosure may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed.

Further, spatially relative terms, such as “beneath,” “below,” “lower,” “above,” “upper” and the like, may be used herein for ease of description to describe one element or feature's relationship to another element(s) or feature(s) as illustrated in the figures. The spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. The apparatus may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein may likewise be interpreted accordingly.

Terms such as “about,” “roughly,” “substantially,” and the like may be used herein for ease of description. A person having ordinary skill in the art will be able to understand and derive meanings for such terms. For example, “about” may indicate variation in a dimension of 20%, 10%, 5% or the like, but other values may be used when appropriate. A large feature, such as the longest dimension of a semiconductor fin may have variation less than 5%, whereas a very small feature, such as thickness of an interfacial layer may have variation of as much as 50%, and both types of variation may be represented by the term “about.” “Substantially” is generally more stringent than “about,” such that variation of 10%, 5% or less may be appropriate, without limit thereto. A feature that is “substantially planar” may have variation from a straight line that is within 10% or less. A material with a “substantially constant concentration” may have variation of concentration along one or more dimensions that is within 5% or less. Again, a person having ordinary skill in the art will be able to understand and derive appropriate meanings for such terms based on knowledge of the industry, current fabrication techniques, and the like.

Semiconductor fabrication generally involves the formation of electronic circuits by performing multiple depositions, etchings, annealings, and/or implantations of material layers, whereby a stack structure including many semiconductor devices and interconnects between is formed. Dimension scaling (down) is one technique employed to fit ever greater numbers of semiconductor devices in the same area. However, dimension scaling is increasingly difficult in advanced technology nodes. Cleanroom contaminant level tolerance is increasingly stringent to ensure sufficient yield as dimensions are scaled down.

Airborne molecular contamination (AMC) is overly represented in out-of-control (OOC) and out-of-specification (OOS) events. A variety of tools and methods are available for detecting increases in contaminant levels in the cleanroom, but are generally insufficient in many respects. A gas chromatography mass spectrometer (GC-MS) provides inline global monitoring of a large spectrum of contaminants, but is generally very slow, and may only be able to accommodate a single sampling point per processing zone. Patrolling operators may also carry or push around offline contaminant sampling devices, but these devices may only detect a small subset of the contaminants, such as acids, bases, and/or hydrochlorofluorocarbons (HCFCs), and are unable to detect total volatile organic compounds (TVOC). As such, detection and response to rises in AMC is generally accomplished with a significant delay, which increases likelihood of an OOC event becoming an OOS event, which can lead to stopping production while a tool or tool accessory causing the rise in AMC is identified, diagnosed, and repaired.

In embodiments of the present disclosure, time-of-flight mass spectrometry (TOF-MS) is employed, which measures various AMC parameters on a qualitative and/or quantitative basis, incorporating refrigerant byproducts (e.g., chlorofluorocarbons, hydrofluorocarbons, perfluorocarbons, or the like), isopropyl alcohol (IPA), acetone and general TVOC detection in minutes at hundredths of parts-per-billion (ppb) accuracy, and can reflect sudden leakage events with quick identification of chemical species.

Contaminant distribution and peak concentration level location may be calculated by computational fluid dynamics (CFD) techniques according to tool layout, and results recorded in a database. When AMC leakage occurs in the cleanroom, contaminants are collected by a sampler array and analyzed by the TOF-MS to generate an AMC concentration distribution map. Comparison between the AMC concentration distribution map and the calculated results may be used to predict a location (or tool) as the source of the leakage. The predicted location (or tool) may be further compared against tool information to ensure accurate prediction. The tool information may include signals indicating acid and/or solvent supply send from the tool, which may be stored in a factory-side SCADA via a system integration (SI) network. The tool information may further include tool running status from a fault detection system and/or tool utility information from an electronic bluebook (“e-Bluebook”). After comprehensive comparison and judgment, for example by artificial intelligence (AI)/machine learning (ML), the predicted AMC source can be identified and confirmed. If prediction results are inconsistent with the advanced comparison including the tool information just mentioned, the next most probable location/tool may be taken as the source of the leakage, and the same advanced comparison may be repeated until a match is found.

As such, quasi real-time detection and response can be achieved. The embodiments described may be deployed in sensitive process zones, such as etch, electrochemical plating (ECP)/seed deposition, bench or the like for local monitoring. In some embodiments, AMC real-time information is integrated into a centralized system, such as a supervisory control and data acquisition (SCADA) system, to generate contour maps and animations automatically, and issue alarms in AMC hot zones.

FIG.1Ais a schematic view of system100with AMC management, according to various embodiments of the disclosure. The system100may be configured to monitor contaminant levels in a cleanroom, dispatch automated cleaning units to a source of contaminant leakage, activate the automated cleaning units to lower the contaminant levels, halt transfer of front opening unified/universal pods (FOUPs) to a tool if it is the source, and stop production by the tool.

The system100includes a fab120, a sampling system130, an analysis system140and a control center150, that interact with one another in manufacturing and/or services related to manufacturing an IC device. The entities in the system100are connected by a communications network. In some embodiments, the communications network is a single network. In some embodiments, the communications network is a variety of different networks, such as an intranet and the Internet. The communications network includes wired and/or wireless communication channels. In some embodiments, the communications network includes short range asset tracking hardware and software, such as radio-frequency identification (RFID), Bluetooth Low Energy (BLE), Wi-Fi, ultra-wideband (UWB), or the like. In some embodiments, the communications network includes wide range asset tracking hardware and software, such as low-power wide-area network (LPWAN), Long-Term Evolution (LTE), 5th generation mobile network (5G), Global Positioning System (GPS), or the like. Each entity interacts with one or more of the other entities and provides services to and/or receives services from one or more of the other entities. In some embodiments, one or more of the fab120, the sampling system130, the analysis system140and the control center150are owned by a single, larger company. In some embodiments, one or more of the fab120, the sampling system130, the analysis system140and the control center150coexist in a common facility and use common resources.

The fab120includes wafer fabrication tools122(hereinafter “fabrication tools122”) configured to execute various manufacturing operations on a semiconductor wafer such that the IC device is fabricated. In various embodiments, the fabrication tools122include one or more of a wafer stepper, an ion implanter, a photoresist coater, a process chamber, e.g., a chemical vapor deposition (CVD) chamber or low-pressure CVD (LPCVD) furnace, a chemical-mechanical planarization (CMP) system, a plasma etch system, a wafer cleaning system, or other manufacturing equipment capable of performing one or more suitable manufacturing processes as discussed herein.

The fabrication tools122are located in a cleanroom121. The cleanroom121provides an environment designed to maintain an extremely low level of particulates, such as dust, airborne organisms, or vaporized particles, which may be quantified as number of particles per cubic meter. As IC feature sizes shrink, acceptable number and size of particulates decreases accordingly. Further, the cleanroom121may have limits on AMC, such as HCFCs, acids, bases, and other chemical species. As a result, the cleanroom121may include many design features for mitigating particulate and contaminant level increases, such as filtration units, specialized lighting, temperature and humidity controls, airlocks, pressure controls, and the like.

In addition to the above, the cleanroom121further includes cleaning tools123. The cleaning tools123are configured to move to a location in the cleanroom121, filter air locally by an onboard pump/fan and filter(s) to lower contaminant levels, and return to an electrical charging station without human intervention. In some embodiments, the cleaning tools123are automated guided vehicles (AGVs). The cleaning tools123are described in greater detail with reference toFIG.1D. The cleanroom121is described in greater detail with reference toFIG.1B.

The sampling system130collects samples of air from the cleanroom121at a large number of locations distributed throughout the cleanroom121. The sampling system130may include a multi-channel sampling system132, which collects the samples and is in fluidic communication with a TOF-MS142of an analysis system140. In some embodiments, the multi-channel sampling system132includes a multi-channel sampler, a manifold piping sampler, a rotary valve sampler, or the like. The multi-channel sampling system132is described in greater detail with reference toFIG.1C.

The analysis system140receives the samples of air from the sampling system130at the TOF-MS142. The TOF-MS142may perform qualitative and/or quantitative analysis of chemicals in the samples. In some embodiments, the TOF-MS142measures mass and content level of molecular ions in the air of the cleanroom121. For example, the TOF-MS142may detect concentration levels of refrigerant byproducts, IPA, acetone and/or general TVOCs in the samples at an accuracy of better than about 0.05 ppb, such as about 0.02 ppb. Processing of the detection by the TOF-MS142may be faster than about 2 minutes/sample, such as about 1 minute/sample, though other slower or faster detection processing times are also included, for example, due to relative simplicity (faster) or complexity (slower) of detecting various chemical concentrations.

The control center150receives and stores data from the TOF-MS142in a database152. An AMC supervisory control and data acquisition (SCADA)151may receive measurements (e.g., concentration levels) from the TOF-MS142, and write the measurements to the database152. In some embodiments, the AMC SCADA151receives the measurements through a network to which the TOF-MS142and the AMC SCADA151are each connected. In some embodiments, the AMC SCADA151is a control system including at least one computer. The AMC SCADA151may store acid and/or solvent supply send information corresponding to each of the fabrication tools122, for example, through a system integration network. In some embodiments, the AMC SCADA151may further control operation of the TOF-MS142, including activation/deactivation (powering on/off) of the TOF-MS142, order and/or type of measurements performed by the TOF-MS142, and other suitable operation parameters of the TOF-MS142. In some embodiments, the AMC SCADA151performs data processing on the measurements received from the TOF-MS142, which may include writing the measurements to the database152, as described above, and may also include other types of data processing, such as compression/decompression, noise reduction, smoothing, filtering, peak finding, and the like. In some embodiments, the AMC SCADA151performs visual graphic generation and display for the cleanroom121or one or more zones of the cleanroom121.

The database152may be local, cloud-based, or any combination thereof. An integration system153, which may be part of the AMC SCADA151in some embodiments, may read the measurements recorded in the database152, and may generate contour maps corresponding to a layout of the cleanroom121(e.g., global map or zone maps) based on the measurements. An AGV controller154of the control center150may control the cleaning tools123remotely (e.g., through network equipment155) to, for example, relocate to a contaminant hotspot, activate an onboard fan, deactivate the onboard fan, and return to a charging station. In some embodiments, the network equipment155includes at least one of wired and/or wireless communication channels, such as Wi-Fi, ultra-wideband (UWB), low-power wide-area network (LPWAN), Long-Term Evolution (LTE), 5th generation mobile network (5G), or the like. Details related to control of the cleaning tools123are described in greater depth with reference toFIGS.2A-2D.

FIG.1Bis a diagram illustrating a partial floorplan of the cleanroom121in accordance with various embodiments. The fabrication tools122include various fabrication tools122A-122J arranged in a number of zones121A-121J, respectively. Ten zones121A-121J are shown inFIG.1B, however fewer or more zones may also be included in the cleanroom121. The fabrication tools122may include any of deposition (e.g., CVD, PVD, ALD), plating (e.g., electroless copper plating), photolithography, etching, cleaning, and planarizing (e.g., CMP, grinding) tools, or other suitable tools for manufacturing the IC device. Each zone121A-121J may include one or more type of tool, such as only etching tools, or a plating tool and a planarization tool. An aisle1210(or walkway) may extend between the zones121A-121C and the zones121D-121F, and may end abutting the zone121H. Generally, the aisle1210is free of fabrication tools122. Certain of the zones, such as the zones121G,121I,121J, may not abut the aisle1210. Cleaning tools123, which may also be referred to as Automatic Guided Vehicle (AGVs), are located throughout the cleanroom121in the various zones121A-121J. Some zones, such as the zone121A, may have more than one cleaning tool123, such as two cleaning tools123. Certain other zones, such as the zones121I,121J may be free of the cleaning tools123. Some zones, such as the zones121G,121H may share a cleaning tool123.

FIG.1Cis a diagram illustrating a partial side view of the zone121A in accordance with various embodiments. The fabrication tools121A and the cleaning tools123rest on a floor1200, which may be a raised floor in some embodiments. A tool accessory1220in communication (e.g., fluidic, electric, data, or otherwise) with the fabrication tool122A may be located beneath the floor1200. In some embodiments, the tool accessory1220is a chest refrigerator/freezer, which may be used to cool components of and/or materials consumed by the fabrication tool122A.

The multi-channel sampling system132is located beneath the floor1200, in some embodiments. A sampling manifold1210is connected to a plurality of sampling ports1321,1322. The sampling port1322may be directly connected to a port (e.g., an exhaust port) of the tool accessory1220. The sampling ports1321may be connected to air intakes1323. Each set of one sampling port1321connected to an air intake1323may be collectively referred to as a sampling unit1325. In some embodiments, a single sampling port1321may be connected to more than one air intake1323.

InFIG.1B, one or more sampling units1325may be located in each of the zones121A-121J. Certain of the zones121A-121J may include the sampling units1325at a higher density than others of the zones121A-121J. For example, the zone121A may include the sampling units1325at a higher density than the zone121B. While each of the zones121A-121J is illustrated having at least one of the sampling units1325, certain of the zones121A-121J may also be free of the sampling units1325in some other embodiments. The sampling units1325may be arranged in a uniform two-dimensional array, in some embodiments. As shown inFIG.1B, arrangement of the sampling units1325may be aperiodic over the cleanroom121and/or within each of the zones121A-121J. For example, the zone121A may have higher density than the zones121B-121J, and within the zone121A, a high-density sampling region1327may include higher density than other regions within the zone121A. The high-density sampling region1327may correspond to an area of the zone121A that is more sensitive to contaminants, more prone to release of contaminants (e.g., a “hotspot”), or contains more dangerous contaminants (e.g., to yield, to safety, or the like). Density of the sampling units1325as a ratio of area of the cleanroom121may be measured in terms of number of the sampling units1325per one hundred square meters (m2). In some embodiments, the density is in a range of about 1/m2to about 50/m2, or about 10/m2to about 30/m2. Below about 1/m2, sensitivity to contaminant leakage may be insufficient to identify the fabrication tool122or tool accessory1220that is leaking the contaminant.

FIG.1Dis a schematic block diagram of the AGV123in accordance with various embodiments. A drive system123A of the AGV123is configured to move the AGV123in at least two dimensions of space. The drive system123A may include one or more motors, one or more wheels, and one or more axles. The drive system123A receives power (e.g., electrical power) from a power system123B. The drive system123A further receives data, such as control signals for controlling motor rotation velocity, wheel/axle rotation angles and the like, from a control system123F through a data connection, which may include one or more electrical and/or optical signal wires or one or more wireless network ICs.

A filtration system123C of the AGV123is configured to remove contaminants from the air of the cleanroom121. The filtration system123C may include one or more filters123C2, which may include chemical filters, high-efficiency particulate air (HEPA) filters, a combination thereof or the like. The filters123C2are in fluidic communication with one or more fans123C1, which may be circular fans, blowers, exhaust fans or the like. In some embodiments, the fans123C1are in fluidic communication with the filters123C2through one or more ducts123C3. In some embodiments, the fans123C1are in fluidic communication with the filters123C2through a housing of the filtration system123C, and no additional duct123C3is present in the filtration system123C. The power system123B provides power to the filtration system123C, for example, to drive rotation of the fans123C1. In some embodiments, the filtration system123C has an onboard power system (not separately illustrated), which may include at least a power monitoring/regulation IC. In such embodiments, the filtration system123C may receive alternating current (AC) power from the power system123B, and the onboard power system of the filtration system123C may convert the AC power to direct current (DC) power for driving the fans123C1. Such a configuration may provide improved modularity of the AGV123, such that the filtration system123C may be easily removed from the AGV123, allowing the AGV123to be used for other purposes in the cleanroom121.

The filtration system123C of the AGV123and the control system123F may be connected by a data connection. In some embodiments, the control system123F of the AGV123may control parameters (e.g., power on/off, fan speed or the like) of the filtration system123C by the data connection. In some embodiments, the filtration system123C reports operation parameters, such as filter condition/age, to the control system123F by the data connection.

A network system123D of the AGV123is configured to transmit and/or receive data from an external network, such as via the network equipment155of the control center150. In some embodiments, the network system123D includes wireless network equipment, such as one or more ICs and one or more antennae capable of enabling data communication over a network using a protocol such as Wi-Fi, 4G/5G, or other suitable data communications protocol. The network system123D is in data communication with the network equipment155, in some embodiments. The network system123D is also in data communication with the control system123F, which may receive commands from the AGV controller154via the network system123D. In some embodiments, the data communication between the network system123D and the control system123F is though one or more wires, such as electrical or optical wires, or through the air via two or more communication ICs (e.g., Bluetooth, NFC, or other similar ICs) located in the network system123D and the control system123F, respectively. Details of the commands and operation of the AGV123are described in greater depth with reference toFIG.2A.

In some embodiments, the AGV123further includes a sensor system123E having one or more sensors configured to provide information about the environment the AGV123operates in to at least the control system123F. In some embodiments, the one or more sensors include at least one of an optical sensor (e.g., a camera, infrared sensor, or the like), a vibration sensor, an air quality sensor, or other suitable sensor. The sensors may enhance the ability of the AGV123to navigate, detect obstacles, or provide real-time, local information about the air quality in the direct vicinity of the AGV123.

The control system123F may include a microcontroller unit (MCU), a computer processing unit (CPU), a graphics processing unit (GPU), input/output (I/O) IC(s), buses, memory, data storage (e.g., a solid state drive, hard disc drive, etc.), and the like. The I/O IC(s) and buses are generally configured to allow one-way or two-way data communication between the control system123F and the drive system123A, the power system123B, the filtration system123C, the network system123D and the sensor system123E, which may be referred to collectively as “the systems123A-123E.” For example, the control system123F may communicate with (e.g., transmit and/or receive data to/from) the systems123A-123E by way of one or more universal serial bus (USB), peripheral component interconnect (PCI) and/or serial advanced technology attachment (SATA) bus. In some embodiments, the control system123F communicates with the systems123A-123E through an optical data bus, such as via fiber optics. Similar buses are present in the systems123A-123E to enable the data communication with the control system123F, in such embodiments.

FIG.2Ais a diagram illustrating a process20for managing contaminants in the cleanroom121in accordance with various embodiments. The process20includes operations200A-200G,220A-200D, certain of which are illustrated in greater detail with reference toFIGS.2B-2D. The process20will be further described according to one or more embodiments. It should be noted that the operations of the process20may be rearranged or otherwise modified within the scope of the various aspects. It should further be noted that additional processes may be provided before, during, and after the process20, and that some other processes may be only briefly described herein.

In operation200A, an AMC online detection system, which may be the system100, scans the cleanroom121, which may include scanning the ambient environment of the cleanroom121and scanning the fabrication tools122. When scanning the ambient environment and/or the fabrication tools122, the system100may scan for chemical compounds, which may include contaminants (e.g., TVOC) and/or process chemicals (e.g., acids, bases, solvents or the like).

In some embodiments, scanning the fabrication tools122includes determining the process chemicals associated with the fabrication tools122. In some embodiments, the process chemicals are recorded in a database, such as the database152. As shown inFIG.2A, the database152may include one or more individual databases, such as an image database152A and a tool database152B. The tool database152B may store information from one or more sources, such as a SCADA system1521(e.g., the AMC SCADA151) that records acid and/or solvent used by the fabrication tools122. The sources may further include a fault detection system1522, a tool utility information log1523(e.g., an electronic bluebook), a tool chemical information record1524, or the like.

In some embodiments, scanning the ambient environment includes one or more individual operations, such as sampling the ambient environment by the multi-channel sampling system132(as described with reference toFIG.1C), and analyzing the samples obtained from the multi-channel sampling system132by the TOF-MS142. An output of scanning the ambient environment may include levels of various elements (e.g., carbon, chlorine, fluorine, and the like) in parts per billion or another suitable measure.

The scanning of the ambient environment may be performed in a variety of different ways. In some embodiments, with reference toFIG.1B, the multi-channel sampling system132may output samples from the sampling units1325in a particular order. For example, the sampling units1325may be ordered by zone121A-121J, such that the multi-channel sampling system132outputs samples from all sampling units1325in a particular zone, such as the zone121A, prior to outputting samples from a different zone, such as the zone121B. In some embodiments, the multi-channel sampling system132may output the samples based on a prioritized list. For example, a first subset of the sampling units1325may be associated with a high occurrence of spikes in contaminant levels, a second subset of the sampling units1325may be associated with a moderate occurrence of spikes, and a third subset of the sampling units1325may be associated with a low occurrence of spikes. As such, samples may be taken from the sampling units1325in the first subset prior to the sampling units1325in the second subset, which may in turn be taken prior to samples from the sampling units1325in the third subset. In some embodiments, the prioritized list is generated based on historical sampling data collected over days, months, or years of sampling the sampling units1325. In some embodiments, the prioritized list is generated using a machine learning algorithm, which may be similar to a process described in greater detail with reference toFIGS.4A-4B. Once all sampling units1325have been sampled, the multi-channel sampling system132may start over.

In some embodiments, the multi-channel sampling system132outputs the samples from the sampling units1325in a random order or a pseudo-random order, and the random order or pseudo-random order may be combined with any of the above techniques. For example, the multi-channel sampling system132may randomly sample the sampling units1325in a certain zone (e.g., the zone121A) until all of the sampling units1325in the zone have been sampled, then may move on to a different zone (e.g., the zone121B) to sample the sampling units1325in that zone randomly until all of the sampling units1325in the different zone have been sampled. In some embodiments, the first subset of the sampling units1325may be sampled randomly, then the second subset may be sampled randomly, then the third subset may be sampled randomly.

In some embodiments, real-time contaminant distribution maps are generated for various AMC contaminants, corresponding to operation200B ofFIG.2A. One distribution map may be generated for each AMC contaminant, and/or a single distribution map may be generated that includes concentration level information associated with multiple AMC contaminants. The distribution maps may be image files in which each pixel of the image file corresponds to a physical location (e.g., a region of a unit area) in the cleanroom121, and has a color and/or brightness level corresponding to the measured level of one or more contaminants. Image files of the type just described may be referred to as heat maps. In some embodiments, the sampling units1325and/or the fabrication tools122are mapped by location onto the pixels of the image file. In some embodiments, a single sampling unit1325may correspond to one or multiple pixels, which may depend on resolution (width*length) of the image file. Similarly, the fabrication tools122may each correspond to many pixels. In some embodiments, each fabrication tool122is represented as one or more point sources in the image file. For example, the fabrication tool122A may include one or more regions (oriented from a top view) that are more prone to leakage, such as input/output ports, exhaust ports, seals, or other similar regions. In such an example, the fabrication tool122A may be represented in the image file as one or more distinct point sources corresponding to the regions more prone to leakage, each point source mapped to one or more pixels of the image file. Based on the color and/or brightness level at each mapped pixel, further interpolation may be performed at pixels not corresponding to any sampling unit1325or fabrication tool122, such that each pixel in the image file may contain a magnitude (color and/or brightness level) corresponding either to a measured level or an interpolated level.

In operation200C, one or more real-time contaminant distribution maps are compared with AMC images stored in a database, such as image database152A. In some embodiments, the AMC images stored in the image database152A include AMC diffusion images generated for different compounds at different concentrations and different times, such as by computation fluid dynamics (CFD) techniques. In some embodiments, CFD calculations are performed according to layout of the fabrication tools122, and the CFD calculations are stored in the image database152A. In some embodiments, when performing a CFD calculation for a certain fabrication tool122, such as the fabrication tool122A, an assumption is made that the fabrication tool122A is a source of a contaminant leak, and a contaminant distribution and peak concentration level location are calculated by the CFD technique(s). As shown inFIG.2A, for each fabrication tool122, the image database152A may store multiple contamination distribution images210A-210N, which may correspond to the contaminant distributions calculated for the fabrication tools122using various conditions, such as the compound, concentration, time, and the like. The peak concentration level locations may be stored in the contamination distribution images210A-210N, or may be stored as separate files, such as text files.

FIG.2Billustrates a diffusion image210in accordance with various embodiments. The diffusion image210as shown includes seven rows and five columns, though more or fewer rows and/or columns may be included in various embodiments. Each column includes seven regions. For example, a first column includes regions230A1-230A7, a second column includes regions230B1-230B7, a third column includes regions230C1-230C7, a fourth column includes regions230D1-230D7and a fifth column includes regions230E1-230E7. Fabrication tools222A1-222A6, which may be the fabrication tools122, are mapped onto the diffusion image210. In some embodiments, the fabrication tools222A1-222A6are not present as data in the diffusion image210, which may be indicated by the dashed lines inFIG.2B. Each of the fabrication tools222A1-222A6corresponds to one or more of the regions230A1-230E7. For example, the fabrication tool222A1corresponds to six regions230A1-230A3,230B1-230B3, the fabrication tool222A2corresponds to three regions230C1-230C3, the fabrication tool222A3corresponds to six regions230D1-230D3,230E1-230E3, the fabrication tool222A4corresponds to four regions230A5,230A6,230B5,230B6, the fabrication tool222A5corresponds to two regions23005,23006, and the fabrication tool222A6corresponds to four regions230D5,230D6,230E5,230E6. Each of the fabrication tools222A1-222A6may correspond to one region or more than six regions depending on size of each fabrication tool222A1-222A6and size of each region.

Contamination distribution images210A-210F associated with the fabrication tools222A1-222A6, respectively, are shown inFIG.2C. Each contamination distribution image210A-210F includes a peak concentration level (shown by hatching) in one of the regions230A1-230E7. The peak concentration level may correspond to a peak concentration of the one or more compounds. For example, contamination distribution image210A corresponding to fabrication tool222A1includes a peak concentration level at region230B3, diffusion image210B corresponding to fabrication tool222A2includes a peak concentration level at region230D3, diffusion image210C corresponding to fabrication tool222A3includes a peak concentration level at region230E3, diffusion image210D corresponding to fabrication tool222A4includes a peak concentration level at region230B7, diffusion image210E corresponding to fabrication tool222A5includes a peak concentration level at region23007, and diffusion image210F corresponding to fabrication tool222A6includes a peak concentration level at region230E7.

Generally, a peak concentration level is present in only one region of each contamination distribution image210A-210F. In some embodiments, each contamination distribution image210A-210F may include a probability associated with each region230A1-230E7. The probability may be a probability that a peak concentration level will occur in the particular region230A1-230E7assuming the conditions (compound, concentration, time) under which the fabrication tool222A1-222A6leaks contaminants. In such a configuration, the hatched regions in the contamination distribution images210A-210F may represent region having the highest probability of a peak concentration level for the conditions. Further description of probabilities is provided with reference to operation200D andFIG.2C.

As described previously, one or more real-time contaminant distribution maps are compared with AMC images stored in a database, such as image database152A, in operation200C as shown inFIG.2C. A distribution map240is shown, having a peak concentration level at a location241indicated by a star. The distribution map240may be determined as described with reference toFIGS.1A-1D.

To determine which fabrication tool222A1-222A6is most probably the source of the peak concentration level at the location241, with reference toFIG.2D, the contamination distribution image210A-210F most closely matching the distribution map240may be selected. In some embodiments, the selection is performed by determining the region230A1-230E7in which the location241is located. For example, in the distribution map240, the location241may have coordinates241C, such as “(1343,384)”. Using a region table230T, the region230A1-230E7corresponding to the coordinates241C may be determined, continuing the example, as the region230D3(see boxed row in the region table230T). Determination of the contamination distribution image210A-210F corresponding to the coordinates241C may further be performed by looking up the region230A1-230E7corresponding to the location241(e.g., the region230D3) in an image table230T. Using the image table230T, and again continuing the example, the diffusion image210B may be selected from the image table230T in the row corresponding to the region230D3. As such, the diffusion image210B may be selected as most closely matching the coordinates241C of the location241exhibiting peak concentration level of the contaminant.

In operation200D, referring again toFIG.2A, having selected the contamination distribution image210A-210F best matching the distribution map240, a highest-probability fabrication tool222A1-222A6may be set as a prime suspect251(seeFIGS.2C,2F) for causing the peak concentration level at the location241in the distribution map240. In some embodiments, setting the prime suspect251may be based on AMC leak probability for each of the fabrication tools222A1-222A6with respect to the contamination distribution image210A-210F best matching the distribution map240. Continuing the example above, and with reference toFIG.2C, a probability map250B may be generated corresponding to the diffusion image210B. In some embodiments, the probability map250B may be or include a probability table250T, as shown inFIG.2E. In some embodiments, the percentages listed in the probability table250T are forecasts/confidence scores generated by a machine learning/artificial intelligence process or inverse method, such as described with reference toFIGS.4A-4B. In such embodiments, the percentages listed for each contamination distribution image210A-210F may exceed 100% in total. Similarly, the percentage totaled across all contamination distribution images210A-210F per fabrication tool222A1-222A6may exceed 100%. As shown inFIG.2E, in operation200D, the fabrication tool222A2has the highest percentage associated with the diffusion image210B. As such, the fabrication tool222A2is set as the prime suspect251in operation200D.

The above description of operations200C,200D with reference toFIGS.2B-2Emay correspond to simplified embodiments of the process20, in which a single peak concentration level of a single contaminant is identified for the distribution map240, and compared with the contamination distribution images210A-210F to find the contamination distribution image210A-210F best matching the peak concentration level in the distribution map240. In some embodiments, a machine learning/artificial intelligence (ML/AI) process, such as described with reference toFIGS.4A-4B, may be used to perform a more complex comparison that may incorporate a larger and more diverse set of inputs, including peak concentration levels and gradients/distributions of multiple contaminants and/or TVOC, information from the tool database152B, and other suitable inputs. The ML/AI process may further be configured to identify at least two simultaneous and different contaminant leaks originating from two or more fabrication tools222A1-222A6. Further description of the ML/AI process is provided with reference toFIGS.4A-4B.

With reference toFIG.2C, following identification and setting of the fabrication tool222A2as the prime suspect251in operation200D, an advanced verification is performed in operation200E. In some embodiments, the advanced verification includes comparing the prime suspect251(e.g., the fabrication tool222A2) against various process utility information. In some embodiments, the process utility information is stored in a tool database152B. The process utility information may include information from one or more sources1521-1524.

As shown inFIG.2C, a first source1521may include information about supply of one or more acid and/or solvents recorded by a SCADA, which may be a factory-side SCADA independent from the AMC SCADA151. In some embodiments, the advanced verification is or includes determining whether the acid and/or solvent was being sent to the fabrication tool222A2when the distribution map240was generated. If the fabrication tool222A2was not supplied with acid/solvent during generation (a portion or all) of the distribution map240, a first mismatch flag may be set indicating a mismatch between the fabrication tool222A2and the distribution map240. If the acid/solvent was supplied during generation of the distribution map240, a first match flag (or simply no flag) may be set, indicating a potential match between the fabrication tool222A2and the distribution map240.

A second source1522may include information from a fault detection system. In some embodiments, the advanced verification is or includes comparing the fabrication tool222A2with information generated by the fault detection system. For example, the fault detection system may indicate operational status (e.g., active, stopped, under maintenance, under repair, or the like) of each of the fabrication tools222A1-222A6. If the information in the tool database152B from the second source1522indicates the fabrication tool222A2was not active (e.g., was stopped, under maintenance, under repair, or the like) for at least a portion (or all) of the time over which the distribution map240was generated, a second mismatch flag may be set indicating a mismatch between the fabrication tool222A2and the distribution map240. If the fabrication tool222A2was active during a portion (or all) of the time over which the distribution map240was generated, as indicated by the tool database152B, a second match flag (or simply no flag) may be set indicating a potential match between the fabrication tool222A2and the distribution map240.

A third source1523may include information from a utility information log, which may be referred to as an electronic utility information (e.g., electronic bluebook, or “e-bluebook.”) In some embodiments, the advanced verification is or includes comparing the contaminant(s) of the distribution map240with materials used by the fabrication tool222A2when the distribution map240was generated. If the contaminant(s) for which the fabrication tool222A2is the prime suspect251for leakage are not associated with the materials listed in the tool database152B by the third source1523, a third mismatch flag may be set indicating a mismatch between the materials used by the fabrication tool222A2and the contaminant(s) the fabrication tool222A2is suspected of leaking. If the contaminant(s) are associated with the materials listed in the tool database152B, a third match flag (or simply no flag) may be set indicating a potential match between the materials used by the fabrication tool222A2and the contaminant(s) of the distribution map240.

A fourth source1524may include information from a tool chemical log. In some embodiments, the advanced verification is or includes comparing chemicals used inside the fabrication tool222A2with the contaminant(s) detected when generating the distribution map240. For example, the tool chemical log may record gas precursors used by certain of the fabrication tools222A1-222A6, e.g., for a chemical vapor deposition (CVD) tool, an atomic layer deposition/etching (ALD/ALE) tool, or the like. If the information in the tool database152B from the fourth source1524indicates chemicals not associated with the contaminant(s) detected when generating the distribution map240, a fourth mismatch flag may be set indicating a mismatch between the fabrication tool222A2and the distribution map240. If the fabrication tool222A2is indicated as using chemicals associated with the contaminant(s) detected when generating the distribution map240, a fourth match flag (or simply no flag) may be set indicating a potential match between the fabrication tool222A2and the distribution map240. The first to fourth mismatch flags may be referred to collectively as “the mismatch flags,” and the first to fourth match flags may be referred to collectively as “the match flags.”

As shown inFIG.2F, the prime suspect251may be compared with the four sources1521-1524. If no mismatch flags are set following the comparison, the fabrication tool222A2may be confirmed as the source of the contaminant leakage, corresponding to the “Yes” branch of decision box200F. If any of the mismatch flags is set in operation200E, the process20may return to operation200A corresponding to the “No” branch of the decision box200F. Optionally, the process20may return to operation200D, and the fabrication tool222A1-222A6having the second highest percentage in the probability table250T may be selected. Continuing the previous example, if the fabrication tool222A2selected initially in operation200D is found to have not been supplied with acid/solvent (first mismatch flag set), been inactive (second mismatch flag set), used materials inconsistent with the detected contaminant(s) (third mismatch flag set) and/or used chemicals inconsistent with the detected contaminant(s) (fourth mismatch flag set), the fabrication tool222A1may be selected as the prime suspect251in a subsequent iteration of operation200D following “No” branching from operation200F, as the fabrication tool222A1has the second-highest percentage in the row associated with the diffusion image210B.

Once the prime suspect251is confirmed in operation200F, one or more actions220A-220D may be taken to address the contaminant leakage, corresponding to operation200G ofFIG.2A. In some embodiments, the actions220A-220D are taken based on peak concentration level under control of an AGV controller154(seeFIG.1A). The actions220A-220D may correspond to one or more thresholds. In the configuration shown inFIG.2A, a first threshold corresponds to a first action220A, a second threshold corresponds to a second action220B, a third threshold corresponds to a third action220C, and a fourth threshold corresponds to a fourth action220D.

The first threshold may be a baseline threshold, in some embodiments. The baseline threshold may be a concentration level below which no action should be taken by the AGVs123. In some embodiments, the baseline threshold is a fixed concentration level in a range of about 0.04 ppb to about 1 ppm, though other ranges may be suitable depending on, for example, process node or safety considerations. In some embodiments, the baseline threshold is dynamic, and may be adjusted according to one or more process parameters, such as ambient temperature, production loading (e.g., number of the fabrication tools122running simultaneously), process node (e.g., certain nodes have higher sensitivity to contaminants than other nodes), or other suitable process parameters. In some embodiments, the baseline threshold is set by the AMC SCADA151. When the peak concentration level of the distribution map240is below the first threshold, scanning of the cleanroom121(e.g., updating/generating of the distribution map240) continues. As long as the peak concentration level is below the first threshold, generally no AGV123is activated for targeted cleaning of the air, and the AGVs123are located at a charging station. The AGVs123may be activated periodically for inspection or other purposes during the first action220A.

The second threshold may be a warning (e.g., “out of warning”) threshold. In some embodiments, the second threshold is a concentration level substantially equal to the first threshold. When the peak concentration level of the distribution map240is above the second threshold, the second action220B is taken. In some embodiments, the second action220B includes transmitting one or more commands to one or more of the AGVs123from the network equipment155to the network system123D of the AGV123by the AGV controller154. The commands may be received by the control system123F, and may include an activate (e.g., power on) command, and/or a dispatch command.

The dispatch command may include a location, which may be a code corresponding to a destination, such as a fabrication tool122or waypoint near the fabrication tool122. In some embodiments, the control system123F may include or have access to an internal database (internal to the AGV123), and may look up routing instructions, coordinates, or other relevant information used for navigating to the fabrication tool122or waypoint. In some embodiments, the dispatch command itself includes relevant information, such as coordinates of the fabrication tool122or waypoint, or routing instructions thereto, such that the control system123F may be free of navigation databases and/or algorithms. In some embodiments, location of the AGV123is tracked, and the AGV controller154may send continuous navigation commands to the AGV123to direct motion of the AGV123toward an intended destination (e.g., the fabrication tool122or waypoint).

In some embodiments, one or more waypoint stations are present in the cleanroom121, such as in each zone121A-121J of the cleanroom121. A waypoint station may include a broadcast system for guiding the AGV123to the waypoint station. In some embodiments, the broadcast system includes an audio system, a visual system, an electronic system, or other suitable broadcast system. In some embodiments, the visual system may include an infrared transmitter. The AGV123may receive a code corresponding to the waypoint station, and may move automatically toward the waypoint station based on a signal from the broadcast system. For example, the AGV123may move toward the waypoint station by detecting an increasing strength of the signal, e.g. an infrared signal, and moving in a direction where the strength of the signal is stronger than previous.

In some embodiments, the waypoint station includes a visual marker, such as a QR code or other visual pattern, and the sensor system123E (e.g., one or more cameras) of the AGV123may be used to detect the visual pattern and guide the AGV123toward the visual pattern. In some embodiments, the broadcast system and the visual marker may be used in conjunction to provide faster, more robust guidance of the AGV123toward the waypoint station.

Upon reaching the destination, the control system123F may stop motion of the AGV123(e.g., by stopping the drive system123A), and may further control the drive system123A to rotate the AGV123to orient the fan123C1and the filter123C2to an appropriate direction for cleaning the air in the vicinity of the location of the AGV123. In some embodiments, the appropriate direction is determined based on the distribution map240, for example, by determining a contour/gradient of flow of the contaminant(s). Based on the contour/gradient of the flow, the control system123F may orient the filter123C2in an upstream direction of the flow and the fan123C1in a downstream direction of the flow. Once the destination is reached, and optionally once orientation of the AGV123is set, without the fan123C1turned on, the AGV123may be considered to be in a standby mode. While in the second action220B, the fan123C1is generally not turned on, as the peak concentration level has not reached the third threshold or the fourth threshold. The fan123C1may be turned on briefly in standby to verify functionality of the fan123C1in preparation for cleaning in the third action220C if the peak concentration level increases to exceed the third threshold.

The third threshold may be a control (e.g., “out of control,” or “OOC”) threshold. In some embodiments, the control threshold is a fixed concentration level in a range of about 0.1 ppb to about 1 ppm, though other ranges may be suitable depending on, for example, process node or safety considerations. In some embodiments, the control threshold is dynamic, and may be adjusted according to one or more process parameters, such as ambient temperature, production loading (e.g., number of the fabrication tools122running simultaneously), process node (e.g., certain nodes have higher sensitivity to contaminants than other nodes), or other suitable process parameters. Generally, the third threshold is higher than the second threshold, whether fixed or dynamic. For example, the third threshold may be higher than the second threshold by a fixed amount, such as 10 ppb, 1 ppb, 0.2 ppb, or another suitable fixed amount. In some embodiments, the third threshold is higher than the second threshold by a percentage, such as about 100%, about 50%, about 20% or other suitable percentage.

When the peak concentration level of the distribution map240exceeds the third threshold, the third action220C is taken. In some embodiments, the third action220C includes activating (e.g., powering on) the fan123C1. In some embodiments, the fan123C1is configured to rotate at one or more speeds (e.g., revolutions per minute, or “RPM”). In some embodiments, the speed of the fan123C1is set at the same time or shortly after activating the fan123C1. In some embodiments, the speed of the fan123C1is dynamically varied based on the peak concentration level. In some embodiments, the speed of the fan123C1is set at a maximum level initially to lower the peak concentration level as quickly as possible. Upon falling below the third threshold, the speed of the fan123C1may be set at an intermediate level lower than the maximum level to maintain the peak concentration level under the third threshold. In some embodiments, the fan123C1may continue to run until the peak concentration level is lowered to the baseline threshold.

FIG.2Gillustrates the third action220C in accordance with various embodiments. A curve270illustrates peak concentration level, and a curve280illustrates a fan speed waveform. Prior to a time t1, the peak concentration level is below the first threshold and/or second threshold, which may be labeled collectively as TB/WinFIG.2G. At time t1, the peak concentration level spikes to above the third threshold TC, while remaining below the fourth threshold Ts. In this situation, upon exceeding the third threshold TC, the AGV controller154may command one or more of the AGVs123to move to the fabrication tool122or waypoint, and turn on the fan123C1immediately upon arrival, or shortly before arrival. When the fan123C1turns on, the speed is set to a maximum speed SMAXat a time t2. A delay may be present between the time t1when the AGV123is dispatched and the time t2when the AGV123arrives and the fan123C1is turned on, which is shown inFIG.2G. In some embodiments, for a less dramatic spike, the AGV123may be standing by at the fabrication tool122or waypoint prior to the peak concentration level exceeding the third threshold TC, such that a much shorter delay is present between the time t1and the time t2, for example the time for detecting the peak concentration level plus the time from signal transmission from the AGV controller154to activation of the fan123C1by the control system123F.

At time t3, the peak concentration level drops below the third threshold TC. In some embodiments, in response to the peak concentration level dropping below the third threshold TC, the fan speed is lowered to an upper intermediate speed S2, which is lower than the maximum speed SMAX. Between the time t3and time t4, the peak concentration level continues to drop, until reaching or going below the first threshold TB/W. In some embodiments, in response to reaching or going below the first threshold TB/W, the fan speed is lowered to a lower intermediate speed S2, which is lower than the upper intermediate speed S1. In some embodiments, the fan123C1may be turned off, such that the fan speed is substantially zero, e.g., 0 RPM. By maintaining the fan speed at the lower intermediate speed S2, the peak concentration level may be controlled to less than, or substantially equal to, the first threshold TB/W.

In the preceding description ofFIG.2G, the fan speed is illustrated using discreet speeds S1, S2, SMAX. In some embodiments, the fan speed may be controlled finely, for example, as a gradient set by pulse width modulation (PWM), analog control, or another suitable technique. In such embodiments, the fan speed may track the peak concentration level, and establish a stable concentration level through, for example, a negative feedback control loop.

In some embodiments, with reference toFIG.2H, during the third action220C, measurement of the peak concentration level by the system100may be focused on a single sampling unit1325or group of sampling units1325, such as in the high-density sampling region1327, while temporarily excluding sampling through remaining sampling units1325of the system100. For example, as shown inFIG.2H, the location241of the peak concentration level may be near three sampling units1325A-1325C. The location241is also located in the high-density sampling region1327. By only sampling a single sampling unit1325(e.g., the sampling unit1325B) or a small group of sampling units1325(e.g., the sampling units1325A-1325C, or all sampling units1325in the high-density sampling region1327), for example, in the vicinity of the location or fabrication tool122associated with the peak concentration level, the AGV controller154may respond more quickly to changes in the peak concentration level without having to wait for other sampling units1325distal the location241to be sampled, which may take as long as 1-2 minutes per sampling unit1325. Such a focused sampling scheme may be advantageous for establishing the negative feedback loop described with reference toFIG.2G.

In some embodiments, the third action220C further includes activating an alarm or notification, which may be routed to manufacturing and/or factory operators. In response to the alarm or notification, which may indicate the fabrication tool122identified as source of the contaminant leak and the associated peak concentration level, the operator may perform an on-site verification of the leak, shut down the fabrication tool122, perform maintenance and/or repair, schedule maintenance and/or repair, or take another suitable action that stops the contaminant leak and/or repairs the contaminant leak.

Referring again toFIG.2A, the fourth threshold may be a specification (e.g., “out of specification,” “OOS”) threshold. The fourth threshold may be set to a level at which the fabrication tool122may be turned off to prevent safety and/or yield incidents. For example, TVOC or individual contaminant concentration may be toxic at certain concentration levels, or may affect yield at other concentration levels. The fourth threshold may be set to a level that is well below a toxic level or yield impairing level, e.g., less than 50%, less than 20%, less than 10% or less than 1% of the toxic level or yield impairing level.

Upon reaching and/or exceeding the fourth threshold, the fourth action220D is taken. In some embodiments, the fourth action220D includes taking measures to stop the leak causing the peak contaminant level above the fourth threshold. In some embodiments, the measures include one or more of stopping FOUP arrival at the fabrication tool122, stopping production by the fabrication tool122, or other suitable measures for stopping the leak. Stopping production may include putting the fabrication tool122in a standby/idle mode, such that the fabrication tool122remains powered on, but does not perform production processes on semiconductor wafers. Stopping production may include powering down the fabrication tool122. Stopping production may be performed automatically by a SCADA, such as a factory-side SCADA. In some embodiments, the fourth action220D includes generating an alarm and/or sending a notification to an operator identifying the fabrication tool122and the peak concentration level, and the operator manually stopping production by the fabrication tool122.

Due to delay between sampling events at the same sampling unit1325, the peak concentration level as measured may appear to spike rapidly from below the first threshold directly to above the fourth threshold, such that a first measurement of a nearby sampling unit1325to the location241is below the first threshold and the immediately subsequent second measurement of the nearby sampling unit1325is above the fourth threshold. As such, if the fourth threshold is exceeded prior to taking the first action220B or the second action220C, the AGV123may not have been dispatched to the location241prior to reaching the fourth threshold. In this situation, the AGV controller154may not dispatch the AGV123in response to the peak concentration level exceeding the fourth threshold, as production by the fabrication tool122is stopped in the fourth action220D. Similarly, if the AGV123has been cleaning the air near the location241under the third action220C, and the fourth threshold is exceeded, in the fourth action220D, the AGV123may be recalled, for example, to a charging station.

FIG.3Aillustrates a flow chart associated with a process30for fabricating an IC device in accordance with various embodiments. The process30may be used in conjunction with the system100and the process20described with reference toFIGS.1A-2H. The process30will be further described according to one or more embodiments. It should be noted that the operations of the process30may be rearranged or otherwise modified within the scope of the various aspects. It should further be noted that additional processes may be provided before, during, and after the process30, and that some other processes may be only briefly described herein.

In operation300, a wafer is positioned in a first fabrication tool (e.g., the fabrication tool222A1,FIG.2B) in a cleanroom (e.g., the cleanroom121,FIGS.1A,1B). In some embodiments, positioning the wafer includes delivering the wafer in a FOUP to a port of the first fabrication tool. A door of the FOUP may open, and a robotic arm may pick up the wafer and transfer the wafer into a chamber of the first fabrication tool. The robotic arm, or a second robotic arm, may position the wafer on a wafer stage in the first fabrication tool for processing, such as deposition, etching, cleaning, annealing or the like.

In operation310, a peak contaminant level may be detected that is above a first threshold in the cleanroom. In some embodiments, the peak contaminant level is detected by an AMC SCADA, such as the AMC SCADA151, based on measurements by an analysis system, such as the analysis system140including the TOF-MS142. In some embodiments, the peak contaminant level is the peak contaminant level of the distribution map240located at the location241. The first threshold in operation310may correspond to the warning threshold or the control threshold described with reference toFIG.2A.

In operation320, a second fabrication tool (e.g., the fabrication tool222B,FIG.2B) is predicted as the source of contaminant(s) of the peak contaminant level. Prediction of the second fabrication tool may be performed using the operations200A-200F of process20described with reference toFIGS.2A-2H.

In operation330, an AGV cleaner, such as the AGV123, is dispatched to a location corresponding to the second fabrication tool. The dispatching may be performed as described for the second action220B ofFIG.2A. In some embodiments, the AGV cleaner is dispatched by an AGV controller, such as the AGV controller154shown inFIG.1A. In some embodiments, the AGV cleaner is dispatched to a position near the location241of the peak contaminant level. For example, the AGV cleaner may be dispatched to a position within less than 10 meters, within less than 5 meters or within less than 1 meter from the location241, in some embodiments. As such, the position to which the AGV cleaner is dispatched may be determined independent of location of the second fabrication tool.

In operation340, the first fabrication tool completes processing of the wafer. In some embodiments, the processing of the wafer by the first fabrication tool is performed while the AGV cleaner is cleaning the air near the location241of the peak contaminant level. In some embodiments, the peak contaminant level is reduced by action of the AGV cleaner through a portion or the entirety of the processing of the wafer by the first fabrication tool. In some embodiments, the AGV cleaner reduces the peak contaminant level to below the baseline threshold prior to completion of the processing of the wafer by the first fabrication tool.

In operation350, the wafer is removed from the first fabrication tool after completion of the processing by the first fabrication tool. In some embodiments, the wafer is transferred from inside the first fabrication tool to a FOUP, which may be the same or different from the FOUP that delivered the wafer to the first fabrication tool, by a robotic arm.

In operation360, the AGV cleaner may be recalled when the peak contaminant level drops below a second threshold, which may be the baseline threshold, in some embodiments. The peak contaminant level may drop below the second threshold due to cleaning by the AGV cleaner, due to stopping the second fabrication tool, or due to fixing the leak in the second fabrication tool, for example.

FIG.3Billustrates a flow chart associated with a process31for fabricating an IC device in accordance with various embodiments. The process31may be used in conjunction with the system100and the process20described with reference toFIGS.1A-2H. The process31will be further described according to one or more embodiments. It should be noted that the operations of the process31may be rearranged or otherwise modified within the scope of the various aspects. It should further be noted that additional processes may be provided before, during, and after the process31, and that some other processes may be only briefly described herein.

In operation301, a wafer is positioned in a fabrication tool (e.g., the fabrication tool222A1,FIG.2B) in a cleanroom (e.g., the cleanroom121,FIGS.1A,1B). In some embodiments, positioning the wafer includes delivering the wafer in a FOUP to a port of the fabrication tool. A door of the FOUP may open, and a robotic arm may pick up the wafer and transfer the wafer into a chamber of the fabrication tool. The robotic arm, or a second robotic arm, may position the wafer on a wafer stage in the fabrication tool for processing, such as deposition, etching, cleaning, annealing or the like.

In operation311, a peak contaminant level may be detected that is above a threshold in the cleanroom. In some embodiments, the peak contaminant level is detected by an AMC SCADA, such as the AMC SCADA151, based on measurements by an analysis system, such as the analysis system140including the TOF-MS142. In some embodiments, the peak contaminant level is the peak contaminant level of the distribution map240located at the location241. The threshold in operation311may correspond to the specification threshold described with reference toFIG.2A.

In operation321, the fabrication tool is predicted as the source of contaminant(s) of the peak contaminant level. Prediction of the fabrication tool may be performed using the operations200A-200F of process20described with reference toFIGS.2A-2H.

In operation331, FOUP transfer to the fabrication tool is stopped, which may correspond to the fourth action220D ofFIG.2A. In some embodiments, the FOUP transfer is stopped by a SCADA system, such as a factory-side SCADA system different from the AMC SCADA151.

In operation341, the fabrication tool completes processing of the wafer. In some embodiments, the processing of the wafer by the fabrication tool is performed while the FOUP transfer is stopped in operation331.

In operation351, the wafer is removed from the fabrication tool after completion of the processing by the fabrication tool. In some embodiments, the wafer is transferred from inside the first fabrication tool to a FOUP, which may be a FOUP located at the fabrication tool prior to stopping the FOUP transfer in operation331.

In operation361, the fabrication tool is repaired. In some embodiments, the fabrication tool is repaired after all in process wafers located in the fabrication tool are finished processing. In some embodiments, in process wafers are removed from the fabrication tool prior to finishing processing, and temporarily stored (e.g., in a FOUP), until repair of the fabrication tool is complete.

In operation371, FOUP transfer to the fabrication tool is resumed following repair of the fabrication tool.

FIGS.4A,4Bare views illustrating prediction of the fabrication tool122causing the peak contaminant level in accordance with various embodiments.FIG.4Ais a block diagram of a system3224, which may be a control system for performing the operations200C,200D of process20, and/or the operations320,321of the processes30,31, respectively. The control system3224may forecast environmental quality/safety parameters of the cleanroom121, and may perform cleanroom environment enhancement based on the forecast parameters. In some embodiments, the control system3224utilizes machine learning to predict which fabrication tool122is the prime suspect for causing a contaminant leak in the cleanroom121.

In one embodiment, the control system3224includes an analysis model3302and a training module3304. The training module3304trains the analysis model3302with a machine learning process. The machine learning process trains the analysis model3302to select the fabrication tool122, in some embodiments. Although the training module3304is shown as being separate from the analysis model3302, in practice, the training module3304may be part of the analysis model3302.

The control system3224includes, or stores, training set data3306. The training set data3306includes simulated CFD data3308, historical contaminant conditions data3310and historical process results data3318. The simulated CFD data3308includes data related to CFD of the contaminant(s). The historical contaminant conditions data3310includes data related to the environment(s) in which the contaminant(s) are measured, such as environmental data associated with the cleanroom121. The historical process results data3318includes data related to wafer quality following fabrication processes by fabrication tools122in the cleanroom121. As will be set forth in more detail below, the training module3304utilizes the simulated CFD data3308, the historical contaminant conditions data3310and the historical process results data3318to train the analysis model3302with a machine learning process.

In one embodiment, the simulated CFD data3308includes data related to computational flow dynamics of the contaminant(s) generated by the fabrication tools122. For example, thousands or millions of calculations of the flow dynamics of the contaminant(s) may be generated by simulating contaminant leakage by the fabrication tools122under the influence of environmental conditions of the cleanroom121. The simulated CFD data3308may include the contamination distribution images210A-210N associated with the fabrication tools122, which may be simulated individually per contaminant, per environmental condition (e.g., ambient temperature, cleanroom layout, production schedule, chemicals/materials used, and the like), per process step, or other suitable parameter. Accordingly, the simulated CFD data3308can include CFD data for a large number of contaminant(s) and operating conditions of the fabrication tools122.

In one embodiment, the historical contaminant conditions data3310include various environmental conditions or parameters during processing of wafers in the cleanroom121. Accordingly, for each fabrication tool122having data in the simulated CFD data3308, the historical contaminant conditions data3310can include the environmental conditions or parameters that were present during processing of wafers. For example, the historical contaminant conditions data3310can include data related to the temperature, pH, humidity, light, acclimation time, vibration, ESD, cleanliness, production schedule, and/or other suitable environmental conditions parameters. The historical contaminant conditions data3310further includes the distribution maps240for each contaminant or TVOC measured by the system100during the processing of the wafers.

In one embodiment, the historical process results data3318include various wafer quality parameters resulting directly or indirectly from a semiconductor fabrication process performed by the fabrication tools122in the cleanroom121. For example, the semiconductor fabrication processes may include one or more of a photoresist coating process, a planarization process, a cleaning process, a deposition process, an etching process, an annealing process or other suitable fabrication processes. In some embodiments, the historical process results data3318may include measurements of various wafer parameters. The measurements may include layer thicknesses, layer uniformity, roughness, cleanness, or other suitable measurements. In some embodiments, the measurements include results of electrical tests, wafer acceptance tests, optical tests or other suitable tests, which may include pass/fail measurements, reliability measurements, data retention measurements or the like. In some embodiments, the historical process results data3318are related to a plurality of previously processed semiconductor wafers. In some embodiments, the historical process results data3318are related to individual semiconductor wafers, individual runs of semiconductor wafers, and/or individual lots of semiconductor wafers.

In one embodiment, the training set data3306links the simulated CFD data3308and/or the historical contaminant conditions data3310with the historical process results data3318. In other words, the CFD calculations in the simulated CFD data3308and/or the environmental parameters in the environmental conditions data3310are linked (e.g., by labeling) to the measurements in the historical process results data3318. As will be set forth in more detail below, the labeled training set data can be utilized in a machine learning process to train the analysis model3302to generate the various forecasts mentioned previously.

In one embodiment, the control system3324includes processing resources3312, memory resources3314, and communication resources3316. The processing resources3312can include one or more controllers or processors. The processing resources3312are configured to execute software instructions, process data, make thin-film etching control decisions, perform signal processing, read data from memory, write data to memory, and to perform other processing operations. The processing resources3312can include physical processing resources3312and/or virtual processing resources3312. The processing resources3312can include cloud-based processing resources including processors and servers accessed via one or more cloud computing platforms.

In one embodiment, the memory resources3314can include one or more computer readable memories. The memory resources3314are configured to store software instructions associated with the function of the control system and its components, including, but not limited to, the analysis model3302. The memory resources3314can store data associated with the function of the control system3224and its components. The data can include the training set data3306, current process conditions data, and any other data associated with the operation of the control system3224or any of its components. The memory resources3314can include physical memory resources and/or virtual memory resources. The memory resources3314can include cloud-based memory resources accessed via one or more cloud computing platforms. In some embodiments, the memory resources3314include the database152.

In one embodiment, the communication resources3316can include wired and wireless communication resources, which can facilitate communication via one or more networks such as wired networks, wireless networks, the Internet, or an intranet. The communication resources3316can enable components of the control system3224to communicate with each other.

FIG.4Bis a block diagram illustrating operational aspects and training aspects of the analysis model3302ofFIG.4A, according to one embodiment. As described previously, the training set data3306includes data related to a plurality of previously processed semiconductor wafers. Each previously processed semiconductor wafer was processed with particular environmental conditions and resulted in a particular processing result. Cleanroom environment sampling data, cleanroom raw material datasheets, cleanroom raw material life time, cleanroom pipeline layout, production schedule, equipment byproduct datasheets, equipment recipe schedule, equipment pipeline aging data, factory layout outside the cleanroom, and daily/weekly/monthly climate forecast, for example, are formatted into a respective conditions matrix3352. The conditions matrix3352includes a plurality of data vectors3354. Each data vector3354corresponds to a particular parameter.

The example ofFIG.4Billustrates a single conditions matrix3352that will be passed to the analysis model3302during the training process. In the example ofFIG.4B, the conditions matrix3352includes nine data vectors3354, each corresponding to a parameter of the semiconductor fabrication process. For condition types that are not naturally represented in numbers, such as raw material names, a number can be assigned to each possible material.

The analysis model3302includes a plurality of neural layers3356a-e. Each neural layer includes a plurality of nodes3358. Each node3358can also be called a neuron. Each node3358from the first neural layer3356areceives the data values for each data field from the conditions matrix3352. Accordingly, in the example ofFIG.4B, each node3358from the first neural layer3356areceives 36 data values because the conditions matrix3352has 36 data scalars (9*4=36). Each neuron3358includes a respective internal mathematical function labeled F(x) inFIG.3B. Each node3358of the first neural layer3356agenerates a scalar value by applying the internal mathematical function F(x) to the data values from the data fields3354of the conditions matrix3352. Further details regarding the internal mathematical functions F(x) are provided below.

Each node3358of the second neural layer3356breceives the scalar values generated by each node3358of the first neural layer3356a. Accordingly, in the example ofFIG.3Beach node of the second neural layer3356breceives four scalar values because there are four nodes3358in the first neural layer3356a. Each node3358of the second neural layer3356bgenerates a scalar value by applying the respective internal mathematical function F(x) to the scalar values from the first neural layer3356a.

Each node3358of the third neural layer3356creceives the scalar values generated by each node3358of the second neural layer3356b. Accordingly, in the example ofFIG.3B, each node of the third neural layer3356creceives five scalar values because there are five nodes3358in the second neural layer3356b. Each node3358of the third neural layer3356cgenerates a scalar value by applying the respective internal mathematical function F(x) to the scalar values from the nodes3358of the second neural layer3356b.

Each node3358of the neural layer3356dreceives the scalar values generated by each node3358of the previous neural layer (not shown). Each node3358of the neural layer3356dgenerates a scalar value by applying the respective internal mathematical function F(x) to the scalar values from the nodes3358of the second neural layer3356b.

The final neural layer includes only a single node3358. The final neural layer receives the scalar values generated by each node3358of the previous neural layer3356d. The node3358of the final neural layer3356egenerates a data value3368by applying a mathematical function F(x) to the scalar values received from the nodes3358of the neural layer3356d.

In the example ofFIG.4B, the data value3368corresponds to the predicted fabrication tool122corresponding to values included in the conditions matrix3352. The predicted fabrication tool122may be represented as a confidence level (e.g., a percentage) in the data value3368. In some embodiments, the final neural layer3356emay generate data values corresponding to the various forecasts described above. The final neural layer3356ewill include a respective node3358for each output data value to be generated.

During the machine learning process, the analysis model compares the predicted fabrication tool122in the data value3368to the actual fabrication tool122that is the source of the contaminant leak, as indicated by the data value3370. As set forth previously, the training set data3306includes, for each set of historical environmental conditions data, historic process results data indicating the characteristics of the semiconductor wafer that resulted from the fabricating process. Accordingly, the data field3370includes the actual contaminant levels present during the fabricating process reflected in the conditions matrix3352. The analysis model3302compares the predicted fabrication tool122from the data value3368to the actual fabrication tool122from the data value3370. The analysis model3302generates an error value3372indicating the error or difference between the predicted fabrication tool122from the data value3368and the actual fabrication tool122from the data value3370. The error value3372is utilized to train the analysis model3302. In some embodiments, the error value3372is a difference in confidence levels (e.g., percentages).

The training of the analysis model3302can be more fully understood by discussing the internal mathematical functions F(x). While all of the nodes3358are labeled with an internal mathematical function F(x), the mathematical function F(x) of each node is unique. In one example, each internal mathematical function has the following form:
F(x)=x1*w1+x2*w2+ . . .xn*w1+b.

In the equation above, each value x1-xn corresponds to a data value received from a node3358in the previous neural layer, or, in the case of the first neural layer3356a, each value x1-xn corresponds to a respective data value from the data fields3354of the reflector conditions matrix3352. Accordingly, n for a given node is equal to the number of nodes in the previous neural layer. The values w1-wn are scalar weighting values associated with a corresponding node from the previous layer. The analysis model3302selects the values of the weighting values w1-wn. The constant b is a scalar biasing value and may also be multiplied by a weighting value. The value generated by a node3358is based on the weighting values w1-wn. Accordingly, each node3358has n weighting values w1-wn. Though not shown above, each function F(x) may also include an activation function. The sum set forth in the equation above is multiplied by the activation function. Examples of activation functions can include rectified linear unit (ReLU) functions, sigmoid functions, hyperbolic tension functions, or other types of activation functions.

After the error value3372has been calculated, the analysis model3302adjusts the weighting values w1-wn for the various nodes3358of the various neural layers3356a-3356e. After the analysis model3302adjusts the weighting values w1-wn, the analysis model3302again provides the reflector conditions matrix3352to the input neural layer3356a. Because the weighting values are different for the various nodes3358of the analysis model3302, the predicted reflectivity3368will be different than in the previous iteration. The analysis model3302again generates an error value3372by comparing the actual fabrication tool3370to the predicted fabrication tool3368.

The analysis model3302again adjusts the weighting values w1-wn associated with the various nodes3358. The analysis model3302again processes the conditions matrix3352and generates a predicted expiration3368and associated error value3372. The training process includes adjusting the weighting values w1-wn in iterations until the error value3372is minimized.

FIG.4Billustrates a single conditions matrix3352being passed to the analysis model3302. In practice, the training process includes passing a large number of conditions matrices3352through the analysis model3302, generating a predicted fabrication tool3368for each conditions matrix3352, and generating associated error value3372for each predicted fabrication tool. The training process can also include generating an aggregated error value indicating the average error for all the predicted fabrication tools122for a batch of conditions matrices3352. The analysis model3302adjusts the weighting values w1-wn after processing each batch of conditions matrices3352. The training process continues until the average error across all conditions matrices3352is less than a selected threshold tolerance. When the average error is less than the selected threshold tolerance, the analysis model3302training is complete and the analysis model is trained to accurately predict the fabrication tool122that is the source of a contaminant leak based on the environmental conditions and/or process conditions. The analysis model3302can then be used to predict leaks and to select environmental and/or process conditions that will result in reduction of leaks or early containment of leaks (e.g., by dispatching and activating the AGV123). During use of the trained model3302, an environmental conditions vector or matrix, representing current environmental conditions for the cleanroom121and/or the fabrication tools122, and having similar format as the conditions matrix3352, is provided to the trained analysis model3302. The trained analysis model3302can then predict a leak that will result from those environmental conditions.

A particular example of a neural network based analysis model3302has been described in relation toFIG.4B. However, other types of neural network based analysis models, or analysis models of types other than neural networks can be utilized without departing from the scope of the present disclosure. Furthermore, the neural network can have different numbers of neural layers having different numbers of nodes without departing from the scope of the present disclosure.

Embodiments may provide advantages. Faster response time (within 1 minute) is possible by utilizing the local TOF-MS142, allowing for earlier and faster response upon prediction or detection of a CxF, IPA/acetone and/or TVOC leakage event manifests. AMC influence on manufacturing may be reduced to a minimum. Monitoring of AMC situations is performed in quasi-real-time, including for the global cleanroom environment and for fabrication tools or accessories per process zone, with areas deviating from baseline, OOC or OOS highlighted. Alarms may be sent to manufacturing operators and factory operators for further action, such as maintenance or repair. Measured data is compared to an image database by an AI-based graphical recognition program, and advanced AMC data comparison verifies selection of the fabrication tool identified as the source of a contaminant leak. An AI-based management program automatically judges and dispatches AMC cleaners carrying a fan and filter to move toward the AMC event area for treatment.

In accordance with at least one embodiment, a method comprises: generating a contaminant distribution map by sampling an environment of a cleanroom; selecting a first fabrication tool of the cleanroom by comparing the contaminant distribution map with at least one diffusion image in a first database; comparing parameters of the first fabrication tool against process utility information in a second database; and when the parameters are consistent with the process utility information, taking at least one action. The one action may include moving a cleaning tool to a location associated with a contaminant concentration of the contaminant distribution map; turning on a fan of the cleaning tool; stopping pod transit to the first fabrication tool; or halting production by the first fabrication tool.

In accordance with at least one embodiment, a method comprises: generating cleanroom contaminants data by sampling cleanroom contaminants by a sampling system; generating a first image based on the cleanroom contaminants data by a time-of-flight mass spectrometer (TOF-MS); selecting a first fabrication tool based on a forecast using the first image and at least one other cleanroom diffusion image; and reducing contaminant concentration near the first fabrication tool by an automated guided vehicle (AGV) dispatched by an AGV controller.

In accordance with at least one embodiment, a method comprises: positioning a wafer in a fabrication tool; detecting a peak concentration level of a contaminant above a first threshold; predicting the fabrication tool as a source of the contaminant; stopping delivery of further wafers to the fabrication tool; completing processing of the wafer by the fabrication tool; removing the wafer from the fabrication tool; lowering the peak concentration level by repairing the fabrication tool; and resuming delivery of the further wafers to the fabrication tool when the fabrication tool is repaired and the peak concentration level is below a baseline threshold lower than the first threshold.

The foregoing outlines features of several embodiments so that those skilled in the art may better understand the aspects of the present disclosure. Those skilled in the art should appreciate that they may readily use the present disclosure as a basis for designing or modifying other processes and structures for carrying out the same purposes and/or achieving the same advantages of the embodiments introduced herein. Those skilled in the art should also realize that such equivalent constructions do not depart from the spirit and scope of the present disclosure, and that they may make various changes, substitutions, and alterations herein without departing from the spirit and scope of the present disclosure.