MPXM2102AS datasheet(19/670 Pages) MOTOROLA

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Motorola Sensor Device Data

www.motorola.com/semiconductors

Statistical Process Control

Motorola’s Semiconductor Products Sector is continually

pursuing new ways to improve product quality. Initial design

improvement is one method that can be used to produce a

superior product. Equally important to outgoing product

quality is the ability to produce product that consistently

conforms to specification. Process variability is the basic

enemy of semiconductor manufacturing since it leads to

product variability. Used in all phases of Motorola’s product

manufacturing, STATISTICAL PROCESS CONTROL (SPC)

replaces variability with predictability. The traditional philos-

ophy in the semiconductor industry has been adherence to

the data sheet specification. Using SPC methods assures

the product will meet specific process requirements

throughout the manufacturing cycle. The emphasis is on

defect prevention, not detection. Predictability through SPC

methods requires the manufacturing culture to focus on

constant and permanent improvements. Usually these

improvements cannot be bought with state-of-the-art equip-

ment or automated factories. With quality in design, process

and material selection, coupled with manufacturing predict-

ability, Motorola produces world class products.

The immediate effect of SPC manufacturing is predict-

ability through process controls. Product centered and

distributed well within the product specification benefits

Motorola with fewer rejects, improved yields and lower cost.

The direct benefit to Motorola’s customers includes better

incoming quality levels, less inspection time and ship-to-

stock capability. Circuit performance is often dependent on

the cumulative effect of component variability. Tightly

controlled component distributions give the customer greater

circuit predictability. Many customers are also converting to

just-in-time (JIT) delivery programs. These programs require

improvements in cycle time and yield predictability achiev-

able only through SPC techniques. The benefit derived from

SPC helps the manufacturer meet the customer’s expecta-

tions of higher quality and lower cost product.

Ultimately, Motorola will have Six Sigma capability on all

products. This means parametric distributions will be

centered within the specification limits with a product

distribution of plus or minus Six Sigma about mean. Six

Sigma capability, shown graphically in Figure 1, details the

benefit in terms of yield and outgoing quality levels. This

compares a centered distribution versus a 1.5 sigma worst

case distribution shift.

New product development at Motorola requires more

robust design features that make them less sensitive to

minor variations in processing. These features make the

implementation of SPC much easier.

A complete commitment to SPC is present throughout

Motorola. All managers, engineers, production operators,

supervisors and maintenance personnel have received

multiple training courses on SPC techniques. Manufac-

turing has identified 22 wafer processing and 8 assembly

steps considered critical to the processing of semiconductor

products. Processes, controlled by SPC methods, that have

shown significant improvement are in the diffusion, photoli-

thography and metallization areas.

Figure 1. AOQL and Yield from a Normal

Distribution of Product With 6

σ Capability

Standard Deviations From Mean

Distribution Centered

Distribution Shifted

± 1.5

± 3 σ 2700 ppm defective

99.73% yield

± 4 σ 63 ppm defective

99.9937% yield

± 5 σ 0.57 ppm defective

99.999943% yield

± 6 σ 0.002 ppm defective

99.9999998% yield

66810 ppm defective

93.32% yield

6210 ppm defective

99.379% yield

233 ppm defective

99.9767% yield

3.4 ppm defective

99.99966% yield

-6

σ -5σ -4σ -3σ -2σ -1σ 0 1σ 2σ 3σ 4σ 5σ 6σ

To better understand SPC principles, brief explanations

have been provided. These cover process capability, imple-

mentation and use.

PROCESS CAPABILITY

One goal of SPC is to ensure a process is CAPABLE.

Process capability is the measurement of a process to

produce products consistently to specification requirements.

The purpose of a process capability study is to separate the

inherent RANDOM VARIABILITY from ASSIGNABLE

CAUSES. Once completed, steps are taken to identify and

eliminate the most significant assignable causes. Random

variability is generally present in the system and does not

fluctuate. Sometimes, these are considered basic limitations

associated with the machinery, materials, personnel skills or

manufacturing methods. Assignable cause inconsistencies

relate to time variations in yield, performance or reliability.

Traditionally, assignable causes appear to be random due

to the lack of close examination or analysis. Figure 2 shows

the impact on predictability that assignable cause can have.

Figure 3 shows the difference between process control and

process capability.

A process capability study involves taking periodic

samples from the process under controlled conditions. The

performance characteristics of these samples are charted

against time. In time, assignable causes can be identified

and engineered out. Careful documentation of the process is

key to accurate diagnosis and successful removal of the

assignable causes. Sometimes, the assignable causes will

remain unclear requiring prolonged experimentation.

Elements which measure process variation control and

capability are Cp and Cpk respectively. Cp is the

specification width divided by the process width or Cp =

(specification width) / 6

σ. Cpk is the absolute value of the

closest specification value to the mean, minus the mean,

divided by half the process width or Cpk = | closest

specification – X /3σ.

Freescale Semiconductor, Inc.

For More Information On This Product,

Go to: www.freescale.com

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