Engineering, education to workplace, is not just about technical knowledge. Rather, who becomes an engineer and why says much about the profession. Engineering has a "diversity" problem. Like all professions, it must narrow the gap between practitioners on the one hand, and their clientele on the other; it must become "culturally competent." Given the current composition of the engineering faculty and the profession's workforce more generally, it behooves engineering education to diversify while assisting current and future practitioners in becoming culturally competent. Programs that work to diversify engineering are reviewed, with research and evaluation-based findings applied to education and workforce practice.

This paper examines the various factors that contribute to the success of minority students in engineering programs by exploring past and current paradigms promoting success and analyzing models for advancing the participation of members of these populations. Included is a literature review of articles, government reports, Web sites, and archives published since 1980. Student success is correlated to several indicators, including pre‐college preparation, recruitment programs, admissions policies, financial assistance, academic intervention programs, and graduate school preparation and admission. This review suggests that the problem of minority underrepresentation and success in engineering is soluble given the appropriate resources and collective national “will” to propagate effective approaches.

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Due to the inherent complexity of the plasma etch process, approaches to modeling this critical integrated circuit fabrication step have met with varying degrees of success. Recently, a new adaptive learning approach involving neural networks has been applied to the modeling of polysilicon film growth by low-pressure chemical vapor deposition (LPCVD)[l]. In this paper, neural network modeling is applied to the removal of polysilicon films by plasma etching. The plasma etch process under investigation was previously modeled using the empirical response surface approach [2]. However, in comparing neural network methods with the statistical techniques, it is shown that the neural network models exhibit superior accuracy and require fewer training experiments. Furthermore, the results of this study indicate that the predictive capabilities of the neural models are superior to that of their statistical counterparts for the same experimental data.

This paper describes and evaluates the effectiveness of a summer undergraduate research program designed to attract qualified minority students into graduate school in electrical engineering. This eight‐week program recruits students of at least junior‐level undergraduate standing on a nationwide basis and pairs them with faculty members and graduate student mentors to undertake research. The research activities are conducted in the School of Electrical and Computer Engineering and National Science Foundation Engineering (NSF) Research Center in Low‐Cost Electronic Packaging at the Georgia Institute of Technology. From 1992–1995, a total of 47 students participated in this program. Thirty‐six of these participants were interviewed by phone to obtain qualitative and quantitative information about the program's impact. The findings indicate that 92% of the program participants are either currently enrolled in a graduate program, plan to attend graduate school in the next two years, or have completed a graduate degree. In comparison to a control group of individuals drawn from the membership of the National Society of Black Engineers (NSBE) alumni, it is found that program participants are more likely to pursue advanced degrees and more likely than non‐participants to continue their studies in engineering. In addition, participants report higher starting salaries than non‐participants. Overall, attitudes toward the program are positive, and the data suggests that GT‐SUPREEM does have a significant impact on the student participants.