Robotic Manipulation of Highly Irregular

7/28/2019 Robotic Manipulation of Highly Irregular

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Journal of Manufacturing ProcessesVol. 4/No. 1

2002

AbstractThe basic technology for a robotic system is developed to

automate the packing of polycrystalline silicon nuggets into afragile fused silica crucible in Czochralski (melt pulling) semi-conductor wafer production. The highly irregular shapes ofthe nuggets and the packing constraints make this a difficultand challenging task. To address this task, key researchareas are identified, developed, and integrated. In this sys-tem, nuggets are grasped by a three-cup suction gripper andmanipulated with a seven-degree-of-freedom SCARAmanipulator. An optical 3-D vision system, based on activelaser triangulation, measures nugget and crucible profiles. Amodel-free Virtual Trial and Error packing algorithm deter-mines optimal nugget placement in real time. A hybrid posi-tion-force control scheme has been implemented and testedfor physical nugget placement. The simulation and laborato-ry tests show that the system has the capabilities of meeting

high production rates, achieving high process constraints,and maintaining cost effectiveness that exceed levelsobtained with manual packing. The results suggest thatmodel-less robotic sensor control systems can be effective inmanufacturing applications. The key contribution of thispaper is to show that robot systems can be effectively usedto manipulate highly irregular shaped objects in the contextof real commercial manufacturing processes.

Keywords: Irregular Object Robot Manipulation, BinPacking, Semiconductor Manufacturing

1. IntroductionThe requirements for growing large, single,

device-grade semiconductor crystals are very strin-gent. Extraordinarily low impurity levels, on theorder of 1 part in 10 billion, require careful handlingand treatment of the material at each step of themanufacturing process. During the Czochralski(melt pulling) semiconductor wafer productionprocess (also known as the CZ process), highlyirregular shaped polycrystalline silicon nuggets

(Figure 1) are packed (charged) into large fusedquartz crucibles (Dubowsky 1998; Sujan andDubowsky 1999; Sujan, Dubowsky, and Ohkami2000; Sujan and Dubowsky 2000). The nuggetsrange in weight from a few grams to about 600grams. Avoiding contamination, protecting the frag-ile glass crucible from damage, and following com-plex packing density rules are key constraints duringthe process (Dubowsky 1998). Once packed, thesecrucibles are placed in ovens, during which the CZmelt pulling process occurs. The extruded semicon-ductor ingots are then sliced into wafers from whichsemiconductor chips are etched. Currently, 45.7 cmdiameter crucibles, which are packed manually, arebeing replaced by much larger (more than 91.5 cmdiameter) crucibles for use in the upcoming fabrica-

Robotic Manipulation of Highly IrregularShaped Objects: Application to a Robot Crucible

Packing System for Semiconductor ManufactureVivek A. Sujan and Steven Dubowsky, Dept. of Mechanical Engineering, Massachusetts Institute of

Technology, Cambridge, Massachusetts, USA

Yoshiaki Ohkami, Dept. of Mechanical Engineering, Tokyo Institute of Technology, Tokyo, Japan

Figure 1Typical Polycrystalline Sil icon Nuggets

(b) Side

(a) Top


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tion of a new generation of 300 mm diameter wafers

(Dubowsky 1998). For these larger crucibles, manu-al packing is neither ergonomic nor practical.Automation has the potential benefit of reducingcost, achieving greater packing consistency, andreducing packing time. Previous studies have con-cluded that because each nugget has unique size andshape, and because packing rules are so strict, fixedautomation was not feasible (Dubowsky 1998).Additionally, studies also show that process modifi-cation, such as nugget crushing and pouring, resultsin reduced yields (Dubowsky 1998). The objectiveof this study was to develop a more flexible robotic

system to automate the crucible packing process.This paper describes the system.

The large variance in size and weight, the irregu-lar shape of the nuggets (seeFigure 1), and the strictprocess requirements result in four key technicalchallenges to automate the crucible packing processwith a robotic system. First, the nuggets are difficultto grasp. Second, nuggets must be placed in accor-dance with their geometry and the proper set ofpacking rules within the process specifications;hence, each nugget and crucible surface must bescanned rapidly and accurately to get surface pro-files before a nugget is placed. Third, the planningsystem must use this profile information to deter-mine the optimal placement of a nugget in a cru-cible. Determining the best location to place eachnugget is necessary due to the importance of thepacking density and complex process constraints.Finally, the irregularly shaped and stiff nuggets mustbe carefully placed against the fragile quartz cru-cible wall without scratching and contaminating the

process. They must also be carefully placed against

other nuggets in the process without disturbing theexisting structure. This is usually accomplished byhandling the larger nuggets individually.

Systems in which robotic manipulators are used topack irregularly shaped objects have been consideredfor the food handling industry and for the bin pickingproblem (Neal, Rowland, and Neal 1997; Trobinaand Leonardis 1995). These systems have consideredsome of the challenges addressed here, but theircapabilities are unable to meet the requirements setby the semiconductor production process. Figure 2shows a schematic of a packed crucible. The packing

rules are set by the technology and the stringent tra-ditional CZ crystal production rules. These packingrules require that the nuggets are packed in layers.Packing is initiated with a bed layer formed by small-er, gravel-sized nuggets. Subsequent nugget layersconsist of larger wall nuggets and internal smallerbulk nuggets. Alternate filling of the bulk and wallnuggets eventually provides a full crucible. Finally,packing is completed with a crown of larger nuggets.Figure 3 shows the system concept developed in thisstudy. During the stratified packing, nuggets areacquired one at a time by the manipulator and passedover the nugget scanner to obtain the surface profile.Simultaneously, the crucible surface profile ismapped by an overhead vision system. A packingalgorithm applies this vision data to compute an idealposition for the nugget. The rules to determine thislocation are described in section 5.

This study showed that the concept could packcrucibles at a higher rate than manual packing, pro-vide better consistency, and eliminate the need for

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Figure 2

Typical Crucible with Packing Constraints

Figure 3

Crucible Charging System Layout


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the operator to perform a physically tedious andunpleasant job (Dubowsky 1998). The analysis sug-gests that a return on investment of less than twoyears could be achieved under full operating condi-tions. This paper describes the design, integration,

and calibration of the grasping/manipulation,vision, placement planning, and control systems forthe robot-assisted crucible packing system. A grip-per, based on vacuum forces for grasping, coupledwith a four-degree-of-freedom Adept-1 manipulatorand three-degree-of-freedom wrist, manipulates thenuggets is described. Two 3-D vision systems usingactive laser triangulation with CCD cameras mapthe crucible and nugget surfaces. An online model-free packing algorithm determines the placement of3-D irregular nuggets in process constraints andlimitations. Using a cost function to determine

nugget placement, complex packing rules and con-straints of the CZ process can be readily included inthe packing algorithm. Finally, a hybridposition/force control algorithm is developed andimplemented for regulating the delicate contactforces while in contact with a stiff and fragile envi-ronment. Experimental results (sections 3-7) andeconomic analysis (section 2) show the system canmeet the requirements for automation of the CZprocess. The details of the system design, the indi-vidual subsystems, and the integrated system arepresented in the following sections. Additionally, a

literature review of related work on each subsystemis also provided. Section 2 reviews the overall sys-tem and presents its economic viability, section 3covers the design of the grasping and manipulationsystem, and section 4 describes the vision systems.Section 5 presents the nugget placement planningalgorithm, section 6 presents the hybrid controlarchitecture of the manipulation system, and sec-tion 7 addresses the full system integration.

2. Overall System DesignFigure 4 shows the factory system with this one

custom-made, SCARA robot. The crucible isbrought into position by a conveyor belt and mount-ed on a support structure, which accurately locates itrelative to the overhead surface mapping system.The system operator removes the nuggets from thesealed bags, sorts them into large nuggets (weightgreater than 60 grams) and small nuggets or bulkfill, and places the sorted nuggets onto a conveyor

system (seeFigure 4). There is a separate conveyorsystem for the large nuggets and the small nuggets.One operator sorts nuggets for two robotic fillingstations. Each robot has its own small and largenugget conveyor system. At the far end of the largenugget conveyor is a target sensor, where the nuggetis picked up by the robot. The small nuggets are con-veyored to two filler bins that are picked up andemptied into the crucible.

The system timing is critical to the economic via-

bility of the system. It takes approximately 6 secondsto pick up the filler bin and empty a bulk fill con-tainer (smooth spreading the fill). For each of thelarger nuggets, it takes a total of 812 seconds for themanipulator to pick up the nugget, pass the nugget toand over the nugget mapping system, place it in thecrucible, and return to the nugget pickup location.

To minimize the packing time, the portion of thepacked surface that has recently been worked on isscanned while the manipulator is away from thecrucible picking up and scanning the next nugget.This local high-resolution (1 mm) scanning is a rec-tangular area 6 inches in width and 36 inches inlength. An entire crucible scan at a low resolution(1.5 cm) is performed after every 10th nuggetplacement to account for unexpected motions/dis-turbances of nuggets. The crucible pack and nuggetsurface maps are used to plan the optimal locationfor the nugget while the robot is moving the nuggetto the crucible. A timing diagram for the packing ofthe 36 inch diameter crucible is shown in Figure 5.

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Figure 4

Factory System Design Using Custom Robot


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It is estimated that it takes 265 minutes (just under412 hours) to pack a 91.5 cm crucible with a chargeof 530 kg. This is faster than the 5 hours estimated,based on the factory data, to perform the cruciblepacking task by hand.

The cost of a two-robot system is estimated atapproximately $500,000. With a 10% annual cost

of capital, this system cost is approximately$50,000. Assuming a typical silicon wafer manu-facturing plant operates 24 hours a day, 365 daysa year, to pack 120,000 kg/month, the labor costs(including benefits) for one operator running twosystems are approximately $472,000/year. Formanual packing, the equipment cost is assumed tobe about $50,000 for the sorting table and cru-cible holding device. With a manual packing timefor the 530 kg packed in the 36-inch crucible of 5hours, the total annual cost for packing 120,000kg/month is about $854,000. The savings areabout $292,000/year. The robotic system pays foritself in less than two years. The robotic systemalso has the potential for greater process consis-tency and reducing worker repetitive stressinjuries for this unpleasant work. The conclusionof this study is that robotic crucible packing ispractical; however, significant technical problemsneed to be solved. These problems and their solu-tions are discussed below.

3. Grasping/Manipulation System

3.1 System Requirements and Design

The irregular silicon nuggets can be qualitativelydivided according to surface shape and quality. Some

nuggets have a characteristic mottled surface texture,while others display especially jagged angles. Forsuccessful grasping and manipulation, the grippermust be able to grasp at least 85% of the large (80grams and above) nuggets, grasp the bulk filling bin,and orient nuggets through 180 yaw and 15pitch and roll for arbitrary nugget-wall contact.

Mechanical gripper designs considered in thisstudy can be grouped into four classes: clampinggrippers, universal grippers, specialty grippers, andvacuum/magnetic grippers (Fan Yu 1982, Wrightand Cutkosky 1985). Universal grippers are

designed to grasp a variety of parts without recon-figuration of the gripper (Mason 1985, Perovskii1986). Both universal and clamping grippers mustgrasp objects using opposed surfaces. Specialtygrippers are often application specifictypically acustomized tool. Vacuum and magnetic grippers canpick up parts using one object face; however, mag-netic grippers can only lift ferric materials andhence are not appropriate for silicon nuggets.Vacuum grippers, although promising for theirinherent grasping compliance, have been appliedtoward irregular objects in only a limited number of

cases (Mannaa, Akyurt, and El-Kalay 1991); howev-er, in this study it was found that a vacuum grippercould be designed to handle the highly irregularshaped nuggets and meet the system constraints.

In developing the vacuum gripper, determiningthe vacuum cup material, size, shape, number, andconfiguration are key design parameters. The vacu-um cup material is selected based on contaminationconstraints. Vacuum cup size, shape, and numberdetermine the maximum lifting force for a givenpressure gradient; however, due to the highly irregu-lar shapes of the nuggets, perfect seals are hard toobtain. Hence, vacuum cup geometry must be deter-mined empirically. Vacuum cup geometry can bedescribed using four criteria: the presence of cleats,the shape of the lip, the depth, and the compliance ofthe vacuum cup. Nugget grasping tests indicate thata bellowed, sharp-lipped, nonshallow, smaller vacu-um cup is the optimum cup.

The gripper features three closely spaced FDA-grade vinyl B3-1 vacuum cups mounted on a mani-

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Figure 5Timing Diagram for Packing a 36 in. Diameter Crucible with One

Large Custom-made SCARA


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fold plate (Figures 6and7). This material was foundto be least likely to contaminate the packing process.A single vacuum line enters the manifold. Flow toeach of the cups is regulated by three restrictionvalves in the manifold. Without flow restriction, asingle unsealed cup would result in power (pressure)loss to the other cups. A pressure transducer addedto the pneumatic lines measures the vacuum pres-sure at a point just before the manifold. A successfulgrasp can then be identified.

The wrist developed to support the gripper iscapable of +30/60 pitch, 90 roll, and continu-ous yaw rotation. Coupled with a four-degree-of-freedom Adept 1 manipulator, the seven-degree-of-freedom system allows for arbitrary nugget place-ment with respect to the crucible. The six-axisforce/torque sensor (seeFigure 7) is used for forcecontrol in manipulating the grasped nugget againstthe crucible wall and previously placed nuggets(see section 6).

3.2 Experimental Validation

To tune the manifold pressure for a successfulgrasp, a calibration set of 20 nuggets was selected. Asuccessful grasp required only two successful cupseals. A maximum downward force of 15 N wasused, at which point the vacuum cups would bottomout. The flow resistance for a cup was tuned by seal-ing two cups and adjusting the restriction valve ofthe open cup until the desired manifold pressure wasreached.Figure 8 shows the number of the nuggets

successfully grasped as a function of manifold pres-sure, for both the two-cup and three-cup seal. Thegreatest number of nuggets was grasped at a vacuumpressure of .76 atmospheres.

For low manifold pressures (low flow resistance),grasping was unsuccessful in the two-cup trialsbecause the unsealed vacuum cup power loss pre-vented enough pressure from being applied to thenuggets. For the three-cup trials at low pressure, thehigher air flow enhanced the ability of three cups toseal onto very rough surfaces of nuggets. At highermanifold pressures, the grasping performance forthe three-cup gripper fell due to low air flow,decreasing sealing ability. Once calibrated for opti-mum manifold pressure, the gripper grasped 90.9%of all nuggets in one attempt and 98.1% in twograsping attempts.

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Figure 6Gripper Pneumatic System Schematic

Figure 7Photograph of End Effector

Figure 8Pressure Test Results


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4. 3-D Vision System

4.1 System Requirements and Design

The two vision systems provide, respectively, the3-D surface geometry of the individual nuggets and

of the surface of nuggets already in the crucible.The requirements for the nugget mapping systemare:X, Y, Zresolution of 1 mm and a mapping timeof 2-3 seconds. The requirements for crucible sur-face mapping system are: X, Y, Z resolution of 1mm and a mapping time of about 4-5 seconds. Itmust also be able to look down from a position thatdoes not interfere with the manipulator (Sujan andDubowsky 1999).

The CZ process contains constraints, such as acomplex environment, cost, and compactness,which make the practical design of 3-D surface

geometry measurements very challenging. A num-ber of methods to obtain visual 3-D data of anobject were considered, including triangulationmethods, holographic interferometry (phase shiftmeasurement), radar (time of flight), lens focus,and Moir techniques (Besl 1989, Hsueh andAntonsson 1992, Jarvis 1983). All these methodssuffer some limitations, such as blind regions, com-putational complexity, limited to highly textured orstructured scenes, limited surface orientation,and/or limited spatial resolution (Jarvis 1983). Forthis system, laser triangulation was selected as

being most effective.Laser triangulation can be achieved by a single

camera aligned along a Z-axis with the center of thefront node of the lens located at (0,0,0) giving theorigin of the camera coordinate frame (see Figure9). At a baseline distance b to the left of the camera(along the negativeX-axis), a laser projects a planeof light at an angle a relative to theX-axis baseline.The point (x, y, z) in the scene is projected onto thedigitized image at the pixel (u, v), controlled by thefocal length of the lens, f. The measured quantities(u, v) are used to compute the 3-D coordinates (x, y,z) of the illuminated scene point:

(1)

The X and Y resolution (X, Y) is given by thewidth of the projected image onto the detector divid-ed by detector resolution. The depth resolution (Z),

given image width Wand detector grid size n in pix-els, is given by:

(2)

Based on the requirements, the crucible and themapping systems shown inFigures 10 and11 weredeveloped. A low-resolution global scan of the 45.7cm diameter crucible withXYresolution of 1.5 cmtakes one second to process. A high-resolution localscan (15 cm wide band) withXYresolution of 1 mmtakes 4.5 seconds to process. Laser scanner timesare negligible. The nugget mapping system consistsof a fixed laser and camera over which the manipu-lator passes the nugget at a constant speed of 3cm/s. Careful synchronization of the manipulator

motion and the mapping system are required. Anominal nugget (7.5 cm 7.5 cm) takes 2.5 secondswith an XY resolution of 1 mm, based on a videoframe rate of 30 frames/second. Critical parametersfor both systems (such as inter camera-laser dis-tances, camera field of view, incident laser angles,etc.) can be determined from a trade-off studybetween the system requirements.

4.2 Calibration

Imperfections in the lens or the detector can resultin image distortions. Intrinsic camera calibrationmaps the errors in image plane coordinates (u, v)and then uses this to compensate the measured val-ues to produce accuracy equal to the resolution ofthe system. The process for intrinsic calibration hasbeen adapted from Hseuh and Antonsson (1992).Given a known horizontal incident angle and ver-tical incident angle between the light ray and theprincipal axis, and a known principal distancef, theexpected image plane coordinate (u, v) can be cal-

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Figure 9Project and Viewing Schematic


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culated. Subtracting these from their actual valuesgives the error map value.

(3)

(4)

(5)

This scheme assumes thatEu at = 0 andEv at = 0are 0. For nonideal lenses the focal length, f, wouldhave to be mapped as an average value given byHsueh and Antonsson (1992):

(6)

Using binary interpolation, anEu andEv can be lookedup for every (u, v) pair, and compensations made.

Also, in practice, the extrinsic parameters of thesystem, such as lens focal lengthf, inter camera-laserdistance b, and mounting geometry with respect tothe ground, are not well known, and need to be cal-culated. By scanning a known object at two knownscan angles, six independent equations in six vari-

ables are generated and solved (Sujan and Dubowsky1999). The geometry shown inFigure 12 yields:

(7)

(8)

(9)

(10)

(11)

(12)

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Figure 12Crucible Mapping System Extrinsic Calibration Geometry

Figure 10

Crucible Mapping System Design Parameter

Figure 11Nugget Mapping System Design


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where g (measurable), , (predefined scanangles), andh (object height) are known. The vari-able x is defined as the distance between the inter-section points of the two incident rays, at angles and, with the extended camera lens front nodal

plane. These equations are solved for multiple mea-sured height pairs and averaged to obtain solutionsfor the extrinsic variables.

4.3 Experimental Validation

In mapping the nugget field inside the crucible, a15 KHz data acquisition rate is obtained, given avideo rate of 30 frames per second and CCD reso-lution of 500 500 pixels on a Pentium 166 MHzsystem (seeFigures 13 and14). This yields a map-ping time of 4.5 seconds for 45.7 cm diameter cru-cible with 1 mm resolution. Low-cost improve-

ments in resolution and computational speeds of thecrucible mapping system hardware can substantial-ly increase this data acquisition rate. The precisemeasurement of a nugget field to check the cruciblemapping system accuracy is difficult; however, esti-mates of the systems precision indicate that the cru-cible mapping system meets the required specifica-tions. These estimates were achieved by mapping aknown flat surface.

The nugget mapping system was evaluated usingrepresentative nuggets. Nugget mapping of a char-acteristic dimension of 7.5 cm and 1 mm resolution

takes 2.5 seconds based on the 15 KHz data acquisi-tion rate. Nugget maps generated were comparedwith nugget profiles obtained from a coordinatemeasuring machine. The RMS error between theprofiles generated by the two systems is 0.4 mmwith of 0.2 mm (the accuracy requirement is 1.0mm) (Sujan and Dubowsky 1999).

5. Placement Planning

5.1 Packing Algorithm Requirements

and Design

The crucible is packed in a stratified manner byalternating between placing large nuggets at the walland center bulk placement, finished with a crown(see Figure 2). The packing algorithm must deter-mine where a nugget is placed meeting a set of pack-ing rules, minimizing nugget rejection, and optimiz-ing the charge density profile. Data provided to thepacking algorithm consists of the [x,y,z] maps of the

nugget surface and the packed surface. To meetpacking rate results, a processing time of one secondon the control computer is required to determine anugget position.

Previous work in 2-D and 3-D packing have beenlargely focused on structured objects such as rectan-gles or rectangular solids, respectively (Cheng andPenkar 1995, Coffman and Shor 1993, Dubowsky1998). The algorithms developed are largely off-lineor online processing (Coffman and Shor 1993,Dubowsky 1998). To solve the off-line problem, anumber of algorithms have been proposed, includingdynamic programming, branch-and-bound searching,and heuristic search techniques (Coffman and Shor1993, Dubowsky 1998, Li and Cheng 1992). Whilethey have been shown to produce near-optimal solu-tions, they are at best pseudo-exhaustive in nature,computationally intensive, and impractical when thenumber of objects to be packed is large. Online algo-rithms, such as genetic algorithms, model-based fit-

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Figure 13Nugget Field Image

Figure 14Crucible Mapping System Nugget Field Mapped Profile


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ting, and simulated annealing, have had some successin packing irregularly shaped objects but are compu-tationally intensive (Dubowsky 1998; Hwang, Kao,and Horng 1994). Problem-specific approximationoptimization algorithms have also been developed,but these are not easily applied to other problems.General methods such as first-fit decreasing, har-monic packing, level-oriented packing, with average-case and worst-case behavior studies, can produceacceptable solutions in reasonable time for a numberof applications (Li and Cheng 1992, Whelan andBatchelor 1993); however, they typically requireobject models or complete object geometries.

To meet the system requirements in this applica-tion, a packing algorithm called Virtual Trial andError was developed (Dubowsky 1998, Sujan andDubowsky 2000). In this method, the nugget and theinternal surface of the crucible are represented by anarray of height values in the crucible coordinateframe. Surface voids are identified in the cruciblemap. Feasible voids and the transformation are eval-

uated by a cost function (see Figure 15). Althoughthe exact nugget center of gravity is not known, anestimate for local static stability is performed at thegiven nugget location. Thus, the best location forthe nugget can be identified. This model-less repre-sentation leads to a computationally simple algo-rithm in which changes to the packing rules can bemade by simple changes in the cost function. Anumber of global packing rule primitives have beenproposed for packing problems, including Li andCheng 1992, Sujan and Dubowsky 2000:

Lowest fitpacking a nugget to lowest positionpossible.

Minimum volume fitpacking a nugget into aposition with least excess volume.

First fitpacking a nugget to the first location withexcess volume less than some predefined value.

Contact fitpacking a nugget into a location withthe greatest number of environment-to-nuggetcontact points.

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Figure 15Finding a Locally Stable Configuration


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A global packing strategy may be one or a com-bination of several of the above rule primitives. Forthe CZ packing process, additional packing rules,such as crucible-nugget contact requirements andvariable-density packing through the charge, can beadded directly to the packing strategy. A series of

packing strategies were defined and simulationswere used to determine the best strategy. Their per-formance was evaluated based on charge density, thenumber of nuggets packed successfully out of thenumber presented, and the stability of their place-ment. The performance index (PI) to evaluate thecost function is defined as:

(13)

where dis the mean charge density,N1 is the numberof nuggets presented, N2 is the number of nuggetspacked, ands is a measure of stability. Althougheach individual nugget may be in a locally stableposition, the global pack may be unstable, much likea house of cards (see Figure 16). To deal with thisissue, a global stability metric is defined. Figures16a and17a show the frequency of a given heightvariation during packing about the current mean

height calculated. A narrow range of variationsreflects a more stable and stratified pack. The algo-rithm limits the maximum height above the lowestpoint on the crucible surface profile to which anugget can be placed.

5.2 Packing ResultsA number of cost functions were studied in simu-

lation to evaluate their effectiveness in giving anoptimal pack. Initial simulations were done in 2-Dwith six cost functions formed from the above pack-ing rule primitives. These include (a) lowest fit, (b)lowest fit with the minimum excess area, (c) first fit,(d) lowest first fit, (e) minimized area fit, and (f)minimized area fit weighed by contact fit. Thenugget shapes are approximated by random noncon-vex polygons where the size distribution was basedon measured nugget data (Dubowsky 1998, Sujanand Dubowsky 2000). Table 1 shows the results forthe random distribution of nonconvex polygons. Itcan be seen that the case of lowest-fit packing hasthe best performance index for both the polygonaland rectangular object packing among the cost func-tions considered. The lowest-fit method, unlike theother methods, does not require the explicit use ofthe height-limiting parameter, as the functionimplicitly causes uniform stratified packing. This

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(a)

(b)

Figure 16Packing SimulationLocal Stability Without Global Stability

(a)

(b)

Figure 17Packing SimulationLocal Stability with Global Stability


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helps reduce the percentage of rejected objects andprovides for a more natural packing structure.

In a 3-D simulation, the nugget shapes are approx-imated by random polyhedrons. Simulation resultsfor packing the walls and crown of the 45.7 cm and

91.4 cm diameter crucible yielded an average chargedensity of 48% and 57.5%, respectively (with 15wrist rotations in pitch and roll) using the lowest-fitpacking rule. With bulk filling, the charge densityincreases to 50% and 60%, respectively. It has beensuggested that a controlled variable density throughthe crucible can improve product quality. The use ofthis robotic system should provide the consistency topermit this question to be addressed quantitatively.Computational speeds for placement planning arewithin the 1.0 second per nugget requirement using aPC with a Pentium 166 MHz processor. Further,

object shape and geometry are not influencing fac-tors in the performance of the algorithm.

6. Control System

6.1 Control System Requirements and Design

Crucible charging involves five distinct subtasksfor the control system: nugget acquisition, nuggetscanning, slew motion of nugget to crucible, walland crown building, and bulk filling. Most of themanipulator actions can be accomplished quitewell with conventional position control. Therequirements are:

Nugget acquisitionvertical motion at 15 cm/s;maximum vertical force of 15 N

Nugget scanninghorizontal motion at 3 cm/s;maximum position error of 0.5 mm

Slew motionnugget brought to crucible in 2.5seconds

Wall and crown buildingnugget to cruciblewall < 30 cm/s; contact force while slidingnugget < 5 N

Bulk fillingbulk filler approaches crucible in 2.5seconds; filler shaken at 4 Hz with 2 amplitude

The wall and crown building mode is a challengingcontrol mode. Difficulties arise when the nuggetcomes into contact with previously placed nuggets.Hence, this section will briefly discuss this aspect ofthe control. An algorithm must be used that can reg-ulate force and position without a priori informationof surface orientation. To achieve the combinedforce and position control required during thismode, the well-known hybrid force/position controlwas selected (Dubowsky 1998).

Two major approaches for both contact force and

endpoint position control for manipulators are thehybrid position/force control and impedance con-trol (Craig 1989). In hybrid position/force control,perfect position tracking can be obtained withoutthe generation of excessive contact forces, makingit highly applicable to known stiff environments(Raibert and Craig 1981); however, switching posi-tion and force domains during contact can causeinstability. Various schemes have been devised thatincorporate sensor information to achieveimproved endpoint position and force control with-out introducing the system instabilities (Li 1996,Muto and Shimokura 1993). Impedance controlemploys a dynamic model to create equations ofmotion of a manipulator (Hogan 1984). When con-tact force feedback is incorporated in the model,force control can be achieved. The advantage ofimpedance control is its stability and applicabilityduring the entire contact control task; however, thisleads to large position errors. Hence, the hybridcontrol was selected.

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Table 1

Packing Algorithm Performance Description

Packing Scheme for Random Polygons Mean Charge Number of Number of Stability: Standard Performance% w/o Rotation Objects Objects Deviation About Reference Index(w/ Rotation) Presented Packed (units h)

d N1 N2 (d N2/N1)/

Lowest fit 75.72 (79.22) 206 204 5.663 13.245Lowest fit w/ area minimization 75.37 (78.93) 207 203 6.4583 11.442First fit 66.05 (69.25) 225 175 18.0159 2.851Lowest first fit 65.78 (68.9) 241 173 16.7 2.827Excess area minimization 75.83 (79.26) 232 204 11.3769 5.862Excess area minimization with contact fit 73.88 (76.91) 228 200 14.6023 4.439


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Two hybrid position/force control schemes havebeen implemented and tested. One performsJacobian Transpose control in the position domain;the other performs Jacobian Inverse control in theposition domain. Both schemes perform JacobianTranspose control in the force domain.

Figure 18 shows the implemented JacobianTranspose hybrid position/force control algorithm.The position domain and force domain are represent-ed by the projection matrix P and the complementaryprojection matrix F, respectively. The joint locations qare measured and converted via the kinematic equa-tions (Kin) to endpoint positionx. The gains Kpx, Kdx,and Kix represent stiffness, damping, and integrationgain in the Cartesian task-space. A Jacobian Inverseposition control algorithm can also be structured.

It is important to minimize the approach speed sothat there is no impact damage to the crucible. It is pos-sible that the manipulator loses contact, and this condi-tion must be handled gracefully by prohibiting themanipulator to achieve high speeds. If the manipulator

breaks contact, the integral force controller shown inFigure 18 is replaced by a velocity damping term andan integral positioning term, which brings the manipu-lator back into contact with the surface slowly.

It is important to note that the P and F selectionmatrices are likely to change during a contact con-trol task. Before contact, P is the identity matrix (fullrank) and F is the zero matrix (no rank). After pointcontact without friction, the P matrix loses one rankand the F matrix gains one rank; however, due to theintegrator locations as shown inFigure 18 a discon-tinuous change in P and F does not translate to a

large discontinuity in control input.

6.2 Results

To demonstrate the effectiveness of bothJacobian Inverse and Jacobian Transpose basedalgorithms, two-dimensional simulations were per-formed. The system model consists of a SCARAmanipulator arm in contact with a stiff cylindricalcrucible.Figure 19 shows the results of one simula-tion. Here the end effector moves under pure posi-tion control (P = I, F = 0) with a speed of 0.33 m/sin the y direction until contact is made with thecrucible. Once contact is detected, the systemswitches selection matrices so that there is positioncontrol in thex direction and force control in theydirection.Figure 19 shows the results of an experi-ment in which the end effector is held fixed aftercontact and the contact force is commanded to beregulated at 15 N. While this is much higher thanrequired for the packing problem, it tends to revealany enhanced impact problems on contact. Contact

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Figure 18Hybrid P/F Control, J Transpose

Figure 19

Simulation Results, Endpoint Held Fixed After Contact

Figure 20Experimental Apparatus for the AdeptOne System


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is detected at 4.5 seconds and results in a substan-

tial (but reasonable) initial force spike and oscilla-tion, quickly regulated to 15 N. There is a momen-tary positioning error.

The control system design was also studied exper-imentally.Figure 20 shows the experimental system.Figure 21 shows representative results in which thenugget is held fixed against the vertical wall. Oncecontact is detected, the force profile oscillates for sev-eral seconds and settles on the chosen value of 5 N,which was experimentally determined to be lowenough to avoid scratching the glass. Deviations ofapproximately 0.5 N are the result of sensor noise.

Thez-positioning error was very small in this exam-ple (less than 0.02 mm) because the system was act-ing as a regulator in this direction, and the dynamicsof the prismatic joint (z-direction) are decoupled fromthat of the rest of the manipulator system.

Figure 22 shows the results of a trial in which thenugget is commanded to slide in the vertical (z)direction while in contact with the wall. The desiredcontact force is set at 5 N to avoid surface scratching.The desired position trajectory is a 0.1 Hz sinusoid ofamplitude 1 cm. Contact is detected at approximate-ly one second. The force controller is partly able tomaintain the desired force, with deviations rangingfrom +1 N to 2.5 N. The position controller main-tains the vertical position error to within 2 mm.

The experimental results show that the implement-ed hybrid position/force control algorithms performwell when the position controller acts as a regulator,and show some success with simultaneous motionand force profiles. In cases where the robot loses con-tact with the surface, contact is reestablished safely.

Experimental results suggest that several factors

influence the degradation of the controller when gen-erating simultaneous position and force profiles:dynamic coupling in the force/position domains,force sensor cross-talk, joint friction, and oversimpli-fication of the model. To obtain better performancewith a hybrid controller, elimination of these distur-bances is required. A friction compensation schemesuch as BSC control has been shown to remove manyof these effects (Morel and Dubowsky 1996). A com-plete discussion of this approach and its applicationsto contact problems is beyond the scope of this paper.

7. Laboratory System IntegrationThe laboratory system consists of a robot manip-

ulator and control system, vision/packing system,and wrist/gripper system (seeFigure 23). The pack-ing procedure plays a supervisory role in planningand assigning control to the major subsystems. Thisincludes nugget acquisition, nugget scanning, cru-cible surface mapping, placement planning, nuggetplacement, and bulk filling.

To provide for accurate scheduling, the governingsystem communicates with the four major subsys-tems, either across computers or across programs. Itis recommended that a factory-level system be oper-ated by a central workstation to maintain simplicity.For intercomputer communication, a series of asyn-chronous handshaking protocols have been devel-oped for communication. These include: Trigger nugget mapping module for nugget scan Trigger crucible mapping module for crucible

surface profiling

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Figure 21AdeptOne Nugget Results, Nugget Held Fixed

Figure 22AdeptOne Nugget Results, Nugget in Motion


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Trigger packing algorithm for image extractionand placement

Transfer nugget position and orientation place-ment coordinates to manipulator for placement

The system was implemented and tested on a 166MHz Pentium computer, using the C++ program-ming language. The control code is interrupt-dri-

ven. The computer system multitasks between twoprograms: a slow outer loop (which handles subsys-tem task scheduling and interaction of the systemwith the user), and a faster time-critical inner con-trol loop (which processes the encoder informationand produces an output control commands for themanipulator/gripper and the vision systems).Information is passed between the two loops viadata latching and semaphore. Because the outerloop can be interrupted at any time, including whilewriting data to memory, it is necessary to set upstrict guidelines about the validity of data beingtransferred between the two programs. This is anadded complication inherent in multitasking or par-allel processing.

Because the crucible charging environment canbe damaged rather easily, it is very important toallow user interaction to alter the manipulatorsbehavior online. The system was able to packnuggets at an average rate of one nugget every 10seconds, reaching a charge density of 50% for the

45.7 cm diameter crucibles, allowing the system tostay competitive with human packing.

8. Conclusions

The technology has been developed to enable therobotic packing of crucibles in CZ semiconductorwafer production. Benefits of the system includeelimination of nonergonomic working conditions,shorter crucible packing times, greater crucible pack-ing consistency, and higher productivity and reducedcosts. The design includes the development of a grip-per mechanism, nugget and crucible surface map-ping modules, a nugget packing algorithm, and con-trol of a manipulator with sufficient compliance andaccuracy in the delicate placement of the nugget.

The results of the grasping tests indicate that the

designed gripper can perform its required taskeffectively, with a 98% grasping success. Nuggetmanipulation is attained with a four-degree-of-free-dom SCARA-type manipulator and a three-degree-of-freedom wrist. The noncontact 3-D geometrymeasuring system based on active triangulationmeasures both the nugget geometry profile and theinternal crucible geometry profile, with a resolutionof 1 mm and scanning times of 2.5 seconds and 4.5seconds, respectively. A Virtual Trial and Errorpacking algorithm is developed and tested in simu-lation for cost function optimization. The final

packing algorithm has been applied in simulationand a charge density of 60% for the 91.4 cm diam-eter crucible is achieved. This compares well withthe expected performance of human packing. Ahybrid position-force control scheme has beenimplemented and tested for physical nugget place-ment. Promising results for simultaneous motionand force profiles have been obtained. The integrat-ed system has been shown to achieve charge densi-ties of about 50% for 45.7 cm diameter crucibles.Required precision and cost effectiveness has beendemonstrated. The results suggest that model-lessrobotic sensor control systems can be effective inmanufacturing applications.

Acknowledgments

The technical and financial support of this workby Shin-Etsu Handotai Co. is acknowledged. Alsothe technical contributions of Joseph Calzaretta,Anthony Leier, Melissa Tata, and Dr. Long-ShengYu are appreciated.

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Figure 23Experimental System


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Authors BiographiesVivek Sujan received his BS in physics and mathematics summa cum

laude from Ohio Wesleyan University in 1996, his BS in mechanical engi-

neering with honors from the California Institute of Technology in 1996,

and his MS in mechanical engineering from the Massachusetts Institute of

Technology in 1998. He is currently completing his PhD in mechanical

engineering at MIT. Research interests include the design, dynamics, and

control of electromechanical systems; mobile robots and manipulator sys-

tems; optical systems and sensor fusion for robot control; image analysis

and processing. He has been elected to Sigma Xi (Research Honorary

Society), Tau Beta Pi (Engineering Honorary Society), Phi Beta Kappa

(Senior Honorary Society), Sigma Pi Sigma (Physics Honorary Society),

and Pi Mu Epsilon (Math Honorary Society).

Steven Dubowsky received his bachelors degree from Rensselaer

Polytechnic Institute in 1963 and his MS and ScD degrees from Columbia

University in 1964 and 1971. He is currently a professor of mechanical

engineering at MIT. He has been a professor of engineering and applied

science at the University of California, Los Angeles, a visiting professor

at Cambridge University, Cambridge, England, and a visiting professor at

the California Institute of Technology. During the period from 1963 to

1971, he was employed by the Perkin-Elmer Corporation, the General

Dynamics Corporation, and the American Electric Power Service

Corporation. Dr. Dubowskys research has included the development of

modeling techniques for manipulator flexibility and the development of

optimal and self-learning adaptive control procedures for rigid and flexi-

ble robotic manipulators. He has authored or coauthored nearly 100

papers in the area of the dynamics, control, and design of high-perfor-

mance mechanical and electromechanical systems. Professor Dubowskyis a registered professional engineer in the state of California and has

served as an advisor to the National Science Foundation, the National

Academy of Science/Engineering, the Dept. of Energy, and the US Army.

He has been elected a fellow of the ASME and is a member of Sigma Xi

and Tau Beta Pi.

Yoshiaki Ohkami obtained his PhD in engineering from the Tokyo

Institute of Technology in 1968 and joined the National Aerospace

Laboratory of Japan as a research engineer on spacecraft attitude control

and large space systems (1968-1992). From 1972-74, he worked as a visit-

ing researcher at the University of California at Los Angeles (UCLA) as a

NASA International Fellow. From 1985-1986, he was the deputy director

for the Space Station Program Office at the Science and Technology

Agency. From 1992-2000, he was a professor at the Tokyo Institute of

Technology Dept. of Mechano-Aerospace Engineering. He is currently the

special advisor to the president at the National Space Development Agency(NASDA) of Japan. Major fields of research are dynamics and control of

large space systems, space robotics, and distributed control with computer

networking. He is a member of IEEE, the American Institute for

Aeronautics and Astronautics, the Japan Society of Mechanical Engineers,

the Japan Society for Aeronautical and Space Sciences, and the Robotics

Society of Japan.


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Documents

Robotic Manipulation of Highly Irregular